randomLearner24/179BNLPpaper · Datasets at Hugging Face

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<s>/Pristina-Regular /PTBarnumBT-Regular /Raavi /RageItalic /Ravie /RefSpecialty /Ribbon131BT-Bold /Rockwell /Rockwell-Bold /Rockwell-BoldItalic /Rockwell-Condensed /Rockwell-CondensedBold /Rockwell-ExtraBold /Rockwell-Italic /Rockwell-Light /Rockwell-LightItalic /Rod /RodTransparent /RunicMT-Condensed /Sanvito-Light /...
<s>versioni successive.) /JPN <FEFF30d330b830cd30b9658766f8306e8868793a304a3088307353705237306b90693057305f002000410064006f0062006500200050004400460020658766f8306e4f5c6210306b4f7f75283057307e305930023053306e8a2d5b9a30674f5c62103055308c305f0020005000440046002030d530a130a430eb306f3001004100630072006f0062006100740020304a3...
<s>untitledSentiment Analysis On Facebook Group Using Lexicon Based Approach Sanjida Akter Lecturer in CSE dept. Northern University Bangladesh Dhaka, Bangladesh Sanjida.akter@gmail.com Muhammad Tareq Aziz Software engineer goBD Dhaka, Bangladesh tareq@gob.coAbstract—Internet is one of the primary sources of Big Data. ...
<s>science to management and business intelligence. There are mainly three methods or level of research on Sentiment Analysis. Document level [1]: analyze the overall sentiment expressed in the text and determine if the overall sentiment is positive or negative. Sentence level [2] - examine the sentiment expressed in s...
<s>a judgment, viewpoint, or statement about matters commonly considered to be subjective. “Entity” which are a product, service, topic, issue, person, organization, or event. It is the target of an opinion. “Subjectivity and emotions” which are the state of mind of a person and instinctive responses. Let us use a Face...
<s>there are groups like BDCyclists, FoodBank where people discuss on same kind of topic. we considered “FoodBank” for this work. To collect Data, there is an API for developers in Facebook called Graph API. This API lets programmer to do different kinds of programmatic activities on top of Facebook data. So we wrote a...
<s>satirical or ironic posts. Point to be noted here that people on FoodBank uses a useful pattern to review something. For example: Fig. 2. Ratting pattern on FOODBANK We can observe that sometime people rate foods or location on a scale of 10. This is an interesting keyword to determine what people think about certai...
<s>their Holders” Computing Attitude and Affect in Text: Theory and Applications Volume 20 of the series The Information Retrieval Series pp. 125-141. [3] Wilson, Wiebe, Hwa “Just how mad are you? finding strong and weak opinion clauses” presented at the 19th national conference on Artificial intelligence, pp. 761-767....
<s>/AvantGarde-DemiOblique /AvantGardeITCbyBT-Book /AvantGardeITCbyBT-BookOblique /BakerSignet /BankGothicBT-Medium /Barmeno-Bold /Barmeno-ExtraBold /Barmeno-Medium /Barmeno-Regular /Baskerville /BaskervilleBE-Italic /BaskervilleBE-Medium /BaskervilleBE-MediumItalic /BaskervilleBE-Regular /Baskerville-Bold /Baskerville...
<s>/Futura-Book /Futura-BookOblique /FuturaBT-Bold /FuturaBT-BoldItalic /FuturaBT-Book /FuturaBT-BookItalic /FuturaBT-Medium /FuturaBT-MediumItalic /Futura-Light /Futura-LightOblique /GalliardITCbyBT-Bold /GalliardITCbyBT-BoldItalic /GalliardITCbyBT-Italic /GalliardITCbyBT-Roman /Garamond /Garamond-Bold /Garamond-BoldC...
<s>/Times-Italic /Times-ItalicOsF /TimesNewRomanMT-ExtraBold /TimesNewRomanPS-BoldItalicMT /TimesNewRomanPS-BoldMT /TimesNewRomanPS-ItalicMT /TimesNewRomanPSMT /Times-Roman /Times-RomanSC /Trajan-Bold /Trebuchet-BoldItalic /TrebuchetMS /TrebuchetMS-Bold /TrebuchetMS-Italic /Tunga-Regular /TwCenMT-Bold /TwCenMT-BoldItal...
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<s>Aspect-Based Sentiment Analysis on Small DatasetsAspect-Based Sentiment Analysis Using SemEval and Amazon Datasets By: Tamanna Hasib ID: 17141017 Saima Ahmed Rahin ID: 13301117 Advisor: Mr. Moin Mostakim BRAC University Department of Computer Science and EngineeringTHESIS REPORT Tamanna Hasib, Saima Ahmed Rahin Aspe...
<s>2.7. Feed-Forward Artificial Neural Network ...................................................................... 12 3. Methodology ........................................................................................................................ 13 4. System Implementation ......................................
<s>PAGE 6 OF 37 1. Introduction Opinion mining is the task of gathering huge amounts of data that contain valuable information about people’s views on different topics. Online shopping websites like Amazon.com are of specific interest for this task, as they host hundreds of thousands of user-reviews for tens of thousan...
<s>field. Moreover, nearly all modern phones include software that tries to understand our speech to do certain tasks for us, and gadgets like Amazon’s Alexa and Google Home are already being used as home assistants that understand what we ask them to do. Over the years, many different out-of-the-box solutions have bee...
<s>Tamanna Hasib, Saima Ahmed Rahin Aspect-Based Sentiment Analysis Using SemEval and Amazon Datasets PAGE 9 OF 37 2.3. Word Vectors Word vectors are word representations in form of high-dimensional vectors of real numbers. Each word in a given text corpus can be represented by one vector. The vectors of words which sh...
<s>describes which noun, or which noun belongs to which verb [27]. We hoped that the dependencies on the one hand improve the sentiment analysis, like proposed in [27] and on the other hand help the classifier understand, which aspect belongs to which sentiment [25]. 2.6. Recurrent Neural Network Fig. 6 Layers of a bas...
<s>had to return a probability distribution of aspects in a sentence. However, this probability distribution alone can hardly be used to define how many of the highest probabilities should be used to create the result set of aspects. Therefore, sub-task 1. was introduced to first define how many aspects the second clas...
<s>multiple tools for various tasks. It is the fastest syntactic parser in the world and its accuracy lies within 1% of the best tools that are available [29]. Apart from dependency parsing, it also offers a full-fledged POS tagger. SpaCy was developed as an NLP tool that can be used in production environments. 4.1.2. ...
<s>translated the entity-attribute-combinations into simpler labels, decreasing the total number of aspect labels to 30. As for the sentiment polarity, the labels “positive”, “neutral” and “negative” were used as intended in the SemEval dataset. The second dataset used were around 100,000 amazon reviews on laptops, tab...
<s>vectors were trained using the text file lined out in the section above. After the word vectors were ready, they were matched with the dictionary obtained from the 2500 review sentences. Since words that weren’t present in the review sentences which were THESIS REPORT Tamanna Hasib, Saima Ahmed Rahin Aspect-Based Se...
<s>it was checked if the current word is the main verb of a sub-sentence, meaning a verb that has its own subject. If that was the case, every connection leading to this verb described an own semantic unit within the given sentence, hence a separable sub-sentence. The procedure above worked well, if the sentence contai...
<s>in the input sequence (e.g. word representations for each word in a sentence) for those two layers: Equation 1 LSTM layer calculations ht is the hidden state at time t, ct is the cell state at time t, xt is the hidden state of the previous layer at time t or the input in case of the first layer. it, ft, gt, and ot a...
<s>using its word vectors and POS tags, which were translated into word embeddings. For the training label in this network, we used a one-hot encoding. We first determined the maximum number of aspects in one sentence among the whole dataset and created an array of this length, containing only zeros. For each sentence ...
<s>All parameter combinations were first run using only the sentences as inputs, then using word vectors, then using word vectors and POS tags, and then using word vectors, POS tags and word dependencies. This was done for each of the six recurrent and feed-forward networks, resulting on a total of 24 training and eval...
<s>development using RNN Fig. 16 Aspect sentiment prediction F1 score development using FF-ANN The most notable curves in the sentiment networks were the text-only training processes, since their accuracy increased drastically right from the start. It was very hard to deduce whether it was due to very good training or ...
<s>SemEval and Amazon Datasets PAGE 30 OF 37 5.1.2.3. With Word Vectors and POS Tags The third experiment used the POS tags, along with word vectors applied on the sentence tokens. Since POS tags in contain semantic information which is similar, but not as detailed as the one contained in word dependencies, they were t...
<s>word vectors, P = POS tags, WD = word dependencies) Fig. 17 F1 score comparison of all experiments As expected, the use of word vectors, which is already common practice in many NLP tasks, increased the accuracy compared to only using the input sentences alone in all the implemented neural networks. Moreover, the re...
<s>SemEval and Amazon Datasets PAGE 34 OF 37 Fig. 18 Aspect sentiment prediction F1 score development and trendline using FF-ANN and POS tags 7. Conclusion and Future Work In this work, we have shown that our proposed approach of improving the performance of neural networks for aspect-based sentiment analysis by using ...
<s>THESIS REPORT Tamanna Hasib, Saima Ahmed Rahin Aspect-Based Sentiment Analysis Using SemEval and Amazon Datasets PAGE 36 OF 37 [14] M. Baroni, G. Dinu, G. Kruszewski (2014). Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors. Center for Mind/Brain Sciences, Univ...
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<s>Sequence_105_ID4459.pdfProceedings of the 2019 5th International Conference on Advances in Electrical Engineering (ICAEE), 26-28September, Dhaka, BangladeshA Computational Approach of Recognizing Emotion from Bengali TextsHasan Abid Ruposh and Mohammed Moshiul HoqueDept. of Computer Science & Engineering, Chittagong...
<s>usable compu-tational system is developed that can recognize emotionsfrom Bengali texts. A significant research activities havebeen carried out on emotion recognition in text, especially,in English and European languages. However, a very fewwork are done on sentiment analysis in Bengali text [4][5].In addition, ther...
<s>is done on Bengali blogs and Newstexts at word and sentence level.Most of the previous studies focused on the sentimentdetection in terms of positive, negative or neutral categoriesfrom Bengali blogs, tweets or social media texts. In theproposed approach, our main task is to recognize the basicsix emotions such as h...
<s>test sample islabeled 0, 1, 2, 3, 4, or 5 [depending on the categories].It represents the semantic interpretation of text in the testsample. If the sample text is not labeled within the categoriesthen the system will fails to process it. Fig. 5 shows a sampleoutput of the predicted level of text sample.C. Classifier...
<s>of developed corpus.TABLE ISUMMARY OF THE BENGALI EMOTION CORPUSNumber of documents 1200Number of sentences 3600Number of words 12000Total unique words 21371) Evaluation Measures: In order to evaluate our pro-posed system, we used several evaluation matrices suchas confusion matrix, precision, recall, F1 score and R...
<s>0.63Anger 0.40 0.67Fear 0.69 0.80Surprise 0.72 0.75Disgust 0.71 0.76V. CONCLUSIONThe main purpose of the proposed system is to classify theBengali texts in terms of six basic emotions such as sadness,happiness, fear, anger, disgust, and surprise respectively. Forthis purpose, we developed a emotion corpus of Bengali...
<s>Proc. of Int. Conf. on Advances in SocialNetworks Analysis and Mining. IEEE, 2011, pp. 587–592.[18] D. Dipankar and S. Bandyopadhyay, “Analyzing emotion in blog andnews at word and sentence level,” in Proc. of the 4th Indian Int. Conf.on Artificial Intelligence, 2009, pp. 1402–1414.[19] S. Sriram, , and X. Yuan, “An...
<s>Data Set For Sentiment Analysis On Bengali News Comments And Its Baseline EvaluationInternational Conference on Bangla Speech and Language Processing(ICBSLP), 27-28 September, 2019Data Set For Sentiment Analysis On Bengali NewsComments And Its Baseline EvaluationMd. Akhter-Uz-Zaman AshikDepartment of CSEShahjalal Un...
<s>opinion, that person’sopinion is not judged whether s/he is right or not. So wedecided to create a data set where the data set will not belabeled according to just one person’s opinion to ensure thecredibility of the data set.II. RELATED WORKSentiment Analysis means the characterization of the sen-timent content of ...
<s>Data AnnotationWe wanted to create our own data set with proper attentiongiven to the credibility of the sentiment behind the sentence.When a sentence is labelled, a single tag cannot ensure theactual sentiment of the sentence. A sentence might seemnegative to an individual but might not be for another indi-vidual. ...
<s>the data-set the appropriate metric shouldbe chosen. Four effective measures have been selected for thestudy based on the confusion matrix of the output. These are:Precision(P ) = TP/(TP + FP ) (1)Recall(R) = TP/(TP + FN) (2)Accuracy(A) = (TP +TN)/(TP +TN +FP +FN) (3)F1− Score(F1) = 2.(P.R)/(P +R) (4)Authorized lice...
<s>class of DeepNeural Networks. CNNs are multi-layer perceptrons thatare regularized. Multi-layer perceptrons usually have fullyconnected networks, which can cause over fitting. CNNssolve this issue by taking advantage of hierarchical patternin data and assemble more complex patterns using smallerand simpler patterns....
<s>Language Resources andEvaluation (LREC 2016). Portorož, Slovenia: European LanguageResources Association (ELRA), May 2016, pp. 2703–2709. [Online].Available: https://www.aclweb.org/anthology/L16-1429[5] A. K. Paul and P. C. Shill, “Sentiment mining from bangla datausing mutual information,” in 2016 2nd Internationa...
<s>Sentiment Analysis on Bangladesh Cricket with Support Vector MachineSee discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/329395265Sentiment Analysis on Bangladesh Cricket with Support Vector MachineConference Paper · September 2018DOI: 10.1109/ICBSLP.2018.8554...
<s>polarity of texts as positive, negativeor neutral. And then we shifted our work to detect praise,criticism and sadness. At first we have created a Googleforms and labeled the data with these three classes. Then wepreprocessed and extracted our data as features using TF-IDFand applied machine learning models. For cla...
<s>theseliterature, we have learned the Bengali language Processing.In addition, [11] focused on twitter micro blogging dataand classified positive, negative and neutral from the data.And in [6], they focused on restaurants reviews and classifiedthe polarity of the text. [13] applied several common ma-chine learning te...
<s>stopwords ofso, in, they, but, or all of these as we do not need these wordswhile training our model. In addition, we have manually listedan array for punctuations and Bengali numbers. All of theseunnecessary list of words, numbers and punctuation marksare filtered in the initialization of TF-IDF vectorizer whichis ...
<s>probabilistic model-basedapproach, we have used Multinomial Naive Bayes classifiersto compare and analyze our results.We have chosen 10% data as our random test sets. Then wehave trained our machine learning model with the rest 90%of the dataset. The trained model predicts from the test setswhether a public opinion ...
<s>course we will apply deeplearning theory for our existing system. The most importantpart is that we are working on Bengali language where wehave not done the stemming, spell-checking and Bengali parts-of-speech tagging for our current research. Use of the propernatural language processing will highly improve our sys...
<s>detection ofreviews. Expert Systems with Applications, 36(7):10760–10773, 2009.View publication statsView publication statshttps://www.researchgate.net/publication/329395265</s>
<s>Performance Measurement of Multiple Supervised Learning Algorithms for Bengali News Headline Sentiment ClassificationSee discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/342226447Performance Measurement of Multiple Supervised Learning Algorithms forBengali New...
<s>both combinations where the label and unlabeled data have in mixed.Peoples express their opinion after reading any kinds of text and given the opinion will be negative, positive or neutral. Sentiment analysis helps to appreciate the opinion of providing text documents. News headline is a short text which contains th...
<s>correct prediction result.III. MethodologyMachine learning approaches help to solve NLP problems. In natural language processing important problem such as text analysis, sentiment analysis, speech to text conversion, text summarization, image to text conversion, language to language translation all is solved using m...
<s>length of negative news headline is 12 and the amount of headlines is 2. Minimum length of negative news headline is 2 and the number of headlines is 3. For positive news maximum number of headlines, text length is 14 and the total number of headline 12 where the minimum number of text length is 1 and the amount of ...
<s>means positive headline. Dataset properties resemble given below. a. Total data 1619 b. 11 types of news c. 1109 positive headline and 510 negative headline d. Minimum & maximum word length 1 and 14. Figure 2: Frequency of positive & negative sentiment In figure 2, x-axis contains the frequency of the negative and p...
<s>the headline. Headline sentiment divides two types where 0 means negative headline and 1 means positive headline. Dataset properties resemble given below. a. Total data 1619 b. 11 types of news c. 1109 positive headline and 510 negative headline d. Minimum & maximum word length 1 and 14. Figure 2: Frequency of posit...
<s>machine can’t understand a rare character or word. So in the pre-processing step remove unwanted characters is very important. In Bengali text whitespace, punctuation, some digits are included in unwanted characters.C. Vocabulary Count For vocabulary count, we use Count Vectorizer. It counts the split word which is ...
<s>technique. Because in the dataset label and input-output given. Then define test dataset to remove the unbiased assessment. In the model train, almost 85%data was given and for test dataset, 15% data with 101 random state are defined. E. Machine Learning Algorithms Supervised learning algorithms are used to solve al...
<s>algorithms with a suitable parameter. Briefly discussed in below about uses algorithms.1) Naive Bayes Classifier This algorithm is used to calculate the probability of the classification problem. In our research, we use multinomial NB which is a distinct classifier used for multinomial disposal. Suppose the probabil...
<s>a suitable parameter. Briefly discussed in below about uses algorithms. i. Naive Bayes Classifier This algorithm is used to calculate the probability of the classification problem. In our research, we use multinomial NB which is a distinct classifier used for multinomial disposal. Suppose the probability of the inpu...
<s>of this contraction was added to the dataset text. ii. Stop word remove: In preprocessing removing stop word is very important. Stop word contains the most common word in a text or document. So in natural language processing stop, words are removed from the text for any language modelling. There are many stop words ...
<s>Decision tree for news sentiment. iv. Nearest Neighbors classifier KNN is a non-parametric approach for classification algorithms. Output value calculated by the value of k which means the nearest value of k. where k is a parameter for find related output. k search the closest values for the providing parameter from...
<s>learning algorithms are used to solve all classification problem. The classification problems are following true and false logic. If the predicted input is positive it's true otherwise it’s false. All of the predicted output is depending on the input label. Suppose x is an input variable and y is an output variable....
<s>Trends, 22nd–23rd November, 2019 College of Computing Sciences & Information Technology, Teerthanker Mahaveer University, Moradabad, India238 Copyright © IEEE–2019 ISBN: 978-1-7281-3245-7parameter from the dataset. In our experiment, we use the value k=3 and provide a good result. Each instant is selected by the dis...
<s>type of problem. The hyperplane is used in each support vector machine classifier. Each hyperplane divided each dataset into two-part. The hyperplane is worked based on the kernel where the kernel represents some algebraic calculation. We use SVC kernel for our classification problem. SVC contain a vector classifier...
<s>0.39 69% 1 0.76 0.82 0.79 Decision Tree 0 0.33 0.40 0.36 60% 1 0.73 0.67 0.70 KNN 0 0.45 0.39 0.41 68% 1 0.76 0.79 0.78 Fig. 5: Accuracy Chart for ML AlgorithmsNow we have used another table to check the classification result with a Bangla News headline. Where all of that applied algorithm predicts the accurate outp...
<s>of San Francisco, 2018.[4] Zhang, Wenbin, and Steven Skiena. “Trading strategies to exploit blog and news sentiment.” In Fourth international aAAI conference on weblogs and social media. 2010.[5] Fu Y, Hao JX, Li X, Hsu CH. Predictive Accuracy of Sentiment Analytics for Tourism: A Metalearning Perspective on Chinese...
<s>Analyzing Performance of Different Machine Learning Approaches With Doc2vec for Classifying Sentiment of Bengali Natural LanguageSee discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/332582518Analyzing Performance of Different Machine Learning Approaches WithDo...
<s>interact using Facebook in Bangladesh[4]. A very large number of data has been comprised overthe Internet as a result of enormous dealing with socialmedia platforms which conveys a significant contribution inSentiment analysis (SA) [1]. To be specific, analyzing thereactions by users accumulated from social media co...
<s>or negative review [8]–[10]. Turney et al. used simpleunsupervised learning algorithm which finds average semanticorientation of the phrases form the review containing adjectivesor adverbs [8]. In system [9] Dave et al. trained a classifierusing a self-tagged corpus of reviews form web sites. Pang etal. applied mach...
<s>among “Like” and otherreactions can be expressed as-• Strongly positive correlation with “Love” and “Wow”.• Weakly positive correlation with “Sad” and “Angry”.Although “Like” reaction is the most common, we haveconsidered this as low-effort data from users and ignored itwhile classifying the sentiment polarity of a ...
<s>is represented as a vector where similardocuments have closer values. We used our corpus, preparedusing the process described in the previous subsection to traindoc2vec model. All the labeled sentences from our corpuswere fed into doc2vec model to build its vocabulary. Here eachlabeled sentence contains a list of Be...
<s>achieve more accuracy as therates of accuracy in both training and validation datasets areincreasing significantly over model training epochs. On theother hand, from the plot of loss on both the training and thevalidation datasets represented in Fig. 3 indicates the sign forstopping model training in earlier epoch i...
<s>of the40th Annual Meeting on Association for Computational Linguistics -ACL ’02, pp. 417–424, 2002.[9] K. Dave, S. Lawrence, and D. M. Pennock, “Mining the Peanut Gallery:Opinion Extraction and Semantic Classification of Product Reviews,”in Proceedings of the 12th international conference on World Wide Web(WWW ’03),...
<s>Sentimental Style Transfer in Text with Multigenerative Variational Auto-EncoderInternational Conference on Bangla Speech and Language Processing(ICBSLP), 27-28 September, 2019Sentimental Style Transfer in Text withMultigenerative Variational Auto-EncoderMehedi Hasan PalashDepartment ofComputer Science and Engineeri...
<s>betterresult, for this expands the window of opportunity for gettingmore diverse outputs. Therefore we get more relevant outputs.We use two different decoders to get two types of response.One for positive and another for negative styled output.II. RELATED WORKStyle transfer with non parallel text has been exercisede...
<s>get 2500 negative comments and 2500 positivecomments.B. PreprocessingWhen we work with text data, we have to read stream ofcharacters. Single characters normally don’t mean anything.If we combine them together carefully, they will make sense.Tokenizing means split the stream of characters such that wecan consider th...
<s>positive sentence is small, and vice versa.Fig. 4. Comparing our results with state of the art modelsComparing with the human tested results with theyelp dataset, all the parameters(grammar, context, positiv-ity/negativity) are much higher than all the state of the artmodels. This means that machines couldn’t be abl...
<s>will have many interesting applica-tions, like-personalized chatbots and even making an universallanguage translator.VIII. ACKNOWLEDGEMENTWe would like to thank the NLP group of Shahjalal Univer-sity of Science and Technology for the necessary insight andexpertise that greatly assisted the research.REFERENCES[1] A. ...
<s>dataData DescriptorDatasets for Aspect-Based Sentiment Analysis inBangla and Its Baseline EvaluationMd. Atikur Rahman * and Emon Kumar Dey Institute of Information Technology, University of Dhaka, Dhaka 1000, Bangladesh Correspondence: bsse0521@iit.du.ac.bd (M.A.R.); emonkd@iit.du.ac.bd (E.K.D.)Received: 20 March ...
<s>review of a restaurant revealstwo aspects: service and food. Both aspects have a positive polarity.“The service was excellent and the food was delicious.”As one can see, the name of the aspect categories are explicitly mentioned in this review. A reviewmight also contain implicit categories; for example, “The staff ...
<s>In [5], the author created an ITproduct-review dataset for the ABSA task, in which a total 2200 reviews were contained.The contribution of this paper is as follows:• We have collected and presented two Bangla datasets for ABSA and have made thempublicly available.• We performed statistical linguistic analysis on the...
<s>pages (https://www.facebook.com/BBCBengaliService/; https://www.facebook.com/DailyProthomAlo/). Some comments are collected from two popular Bengali Websites, BBC Bangla (http://www.bbc.com/bengali), and the Daily Prothom Alo (http://www.prothomalo.com). This dataset was collected by the authors of this paper. The c...
<s>in English and also wrote Bangla sentences written in the English alphabet. We did notconsider these opinions in our dataset. In addition, some comments had only emoticons and noother text or words. We also omitted these for our dataset. All of the processes were done manuallyby the authors. The following section de...
<s>define the category and polarity. Comment: এই িপেচ রান করা টাফ, বািলং িনঃসে েহ ভােলা হেয়েছ Participant Voting for Category Voting for Polarity P1 Bowling Positive P2 Bowling Positive P3 Batting Negative P4 Batting Negative P5 Other Neutral P6 Bowling Positive P7 Other Neutral P8 Bowling Positive P9 Bowling Positive ...
<s>the categories with their polarity in our dataset. Table 4 shows an example for this scenario. Participant Voting for Category Voting for PolarityP1 Bowling PositiveP2 Bowling PositiveP3 Batting NegativeP4 Batting NegativeP5 Other NeutralP6 Bowling PositiveP7 Other NeutralP8 Bowling PositiveP9 Bowling PositiveP10 Ba...
<s>NegativeP2 Team NegativeP3 Batting NegativeP4 Batting NegativeP5 Batting NegativeP6 Team NegativeP7 Team NegativeP8 Batting NegativeP9 Team NegativeP10 Team NegativeWe can see from Table 4 that 50% of the evaluators voted for Batting, and 50% of the votes werefor the Team category, both with a negative polarity. As ...
<s>3034 different comments with five different categories, that is, Batting, Bowling, Team, TeamManagement, and Other. Each of the categories contains three different polarities: positive, negative,and neutral. For example, the Batting category contains a total of 583 comments, for which 138 are ofpositive, 389 are of ...
<s>times as the third most frequent, and so on. Figure 1 shows the diagram in which we plotted the words of our Cricket dataset. The plot follows the trend of Zipf’s law. We also calculated the reliability of the annotation process. The value of the intraclass correlation (ICC) was 0.71. Table 7. The complete statistic...
<s>xlsx format. খুব সীিমত আসন আেছ eবং খাদয্ পাoয়ার জনয্ যেথ aেপkা করেত হেব। ambience negative খুব সীিমত আসন আেছ eবং খাদয্ পাoয়ার জনয্ যেথ aেপkা করেত হেব। service negative দাম তুলনামূলকভােব কম। price positive াi িছল মজাদার food positive যিদo খাবারিট চমৎকার িছল, eিট সsা িছল না। food positive যিদo খাবারিট চমৎকার িছল, eিট ...
<s>this paper, we experimented with the first subtask, that is, the extraction ofthe aspect category. We applied three major steps to extract the aspect category. Firstly, preprocessingwas performed on the dataset. After this, we extracted features from the data and finally performedclassification using some popular cl...

<s>/Pristina-Regular /PTBarnumBT-Regular /Raavi /RageItalic /Ravie /RefSpecialty /Ribbon131BT-Bold /Rockwell /Rockwell-Bold /Rockwell-BoldItalic /Rockwell-Condensed /Rockwell-CondensedBold /Rockwell-ExtraBold /Rockwell-Italic /Rockwell-Light /Rockwell-LightItalic /Rod /RodTransparent /RunicMT-Condensed /Sanvito-Light /...

<s>versioni successive.) /JPN <FEFF30d330b830cd30b9658766f8306e8868793a304a3088307353705237306b90693057305f002000410064006f0062006500200050004400460020658766f8306e4f5c6210306b4f7f75283057307e305930023053306e8a2d5b9a30674f5c62103055308c305f0020005000440046002030d530a130a430eb306f3001004100630072006f0062006100740020304a3...

<s>untitledSentiment Analysis On Facebook Group Using Lexicon Based Approach Sanjida Akter Lecturer in CSE dept. Northern University Bangladesh Dhaka, Bangladesh Sanjida.akter@gmail.com Muhammad Tareq Aziz Software engineer goBD Dhaka, Bangladesh tareq@gob.coAbstract—Internet is one of the primary sources of Big Data. ...

<s>science to management and business intelligence. There are mainly three methods or level of research on Sentiment Analysis. Document level [1]: analyze the overall sentiment expressed in the text and determine if the overall sentiment is positive or negative. Sentence level [2] - examine the sentiment expressed in s...

<s>a judgment, viewpoint, or statement about matters commonly considered to be subjective. “Entity” which are a product, service, topic, issue, person, organization, or event. It is the target of an opinion. “Subjectivity and emotions” which are the state of mind of a person and instinctive responses. Let us use a Face...

<s>there are groups like BDCyclists, FoodBank where people discuss on same kind of topic. we considered “FoodBank” for this work. To collect Data, there is an API for developers in Facebook called Graph API. This API lets programmer to do different kinds of programmatic activities on top of Facebook data. So we wrote a...

<s>satirical or ironic posts. Point to be noted here that people on FoodBank uses a useful pattern to review something. For example: Fig. 2. Ratting pattern on FOODBANK We can observe that sometime people rate foods or location on a scale of 10. This is an interesting keyword to determine what people think about certai...

<s>their Holders” Computing Attitude and Affect in Text: Theory and Applications Volume 20 of the series The Information Retrieval Series pp. 125-141. [3] Wilson, Wiebe, Hwa “Just how mad are you? finding strong and weak opinion clauses” presented at the 19th national conference on Artificial intelligence, pp. 761-767....

<s>/AvantGarde-DemiOblique /AvantGardeITCbyBT-Book /AvantGardeITCbyBT-BookOblique /BakerSignet /BankGothicBT-Medium /Barmeno-Bold /Barmeno-ExtraBold /Barmeno-Medium /Barmeno-Regular /Baskerville /BaskervilleBE-Italic /BaskervilleBE-Medium /BaskervilleBE-MediumItalic /BaskervilleBE-Regular /Baskerville-Bold /Baskerville...

<s>/Futura-Book /Futura-BookOblique /FuturaBT-Bold /FuturaBT-BoldItalic /FuturaBT-Book /FuturaBT-BookItalic /FuturaBT-Medium /FuturaBT-MediumItalic /Futura-Light /Futura-LightOblique /GalliardITCbyBT-Bold /GalliardITCbyBT-BoldItalic /GalliardITCbyBT-Italic /GalliardITCbyBT-Roman /Garamond /Garamond-Bold /Garamond-BoldC...

<s>/Times-Italic /Times-ItalicOsF /TimesNewRomanMT-ExtraBold /TimesNewRomanPS-BoldItalicMT /TimesNewRomanPS-BoldMT /TimesNewRomanPS-ItalicMT /TimesNewRomanPSMT /Times-Roman /Times-RomanSC /Trajan-Bold /Trebuchet-BoldItalic /TrebuchetMS /TrebuchetMS-Bold /TrebuchetMS-Italic /Tunga-Regular /TwCenMT-Bold /TwCenMT-BoldItal...

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<s>Aspect-Based Sentiment Analysis on Small DatasetsAspect-Based Sentiment Analysis Using SemEval and Amazon Datasets By: Tamanna Hasib ID: 17141017 Saima Ahmed Rahin ID: 13301117 Advisor: Mr. Moin Mostakim BRAC University Department of Computer Science and EngineeringTHESIS REPORT Tamanna Hasib, Saima Ahmed Rahin Aspe...

<s>2.7. Feed-Forward Artificial Neural Network ...................................................................... 12 3. Methodology ........................................................................................................................ 13 4. System Implementation ......................................

<s>PAGE 6 OF 37 1. Introduction Opinion mining is the task of gathering huge amounts of data that contain valuable information about people’s views on different topics. Online shopping websites like Amazon.com are of specific interest for this task, as they host hundreds of thousands of user-reviews for tens of thousan...

<s>field. Moreover, nearly all modern phones include software that tries to understand our speech to do certain tasks for us, and gadgets like Amazon’s Alexa and Google Home are already being used as home assistants that understand what we ask them to do. Over the years, many different out-of-the-box solutions have bee...

<s>Tamanna Hasib, Saima Ahmed Rahin Aspect-Based Sentiment Analysis Using SemEval and Amazon Datasets PAGE 9 OF 37 2.3. Word Vectors Word vectors are word representations in form of high-dimensional vectors of real numbers. Each word in a given text corpus can be represented by one vector. The vectors of words which sh...

<s>describes which noun, or which noun belongs to which verb [27]. We hoped that the dependencies on the one hand improve the sentiment analysis, like proposed in [27] and on the other hand help the classifier understand, which aspect belongs to which sentiment [25]. 2.6. Recurrent Neural Network Fig. 6 Layers of a bas...

<s>had to return a probability distribution of aspects in a sentence. However, this probability distribution alone can hardly be used to define how many of the highest probabilities should be used to create the result set of aspects. Therefore, sub-task 1. was introduced to first define how many aspects the second clas...

<s>multiple tools for various tasks. It is the fastest syntactic parser in the world and its accuracy lies within 1% of the best tools that are available [29]. Apart from dependency parsing, it also offers a full-fledged POS tagger. SpaCy was developed as an NLP tool that can be used in production environments. 4.1.2. ...

<s>translated the entity-attribute-combinations into simpler labels, decreasing the total number of aspect labels to 30. As for the sentiment polarity, the labels “positive”, “neutral” and “negative” were used as intended in the SemEval dataset. The second dataset used were around 100,000 amazon reviews on laptops, tab...

<s>vectors were trained using the text file lined out in the section above. After the word vectors were ready, they were matched with the dictionary obtained from the 2500 review sentences. Since words that weren’t present in the review sentences which were THESIS REPORT Tamanna Hasib, Saima Ahmed Rahin Aspect-Based Se...

<s>it was checked if the current word is the main verb of a sub-sentence, meaning a verb that has its own subject. If that was the case, every connection leading to this verb described an own semantic unit within the given sentence, hence a separable sub-sentence. The procedure above worked well, if the sentence contai...

<s>in the input sequence (e.g. word representations for each word in a sentence) for those two layers: Equation 1 LSTM layer calculations ht is the hidden state at time t, ct is the cell state at time t, xt is the hidden state of the previous layer at time t or the input in case of the first layer. it, ft, gt, and ot a...

<s>using its word vectors and POS tags, which were translated into word embeddings. For the training label in this network, we used a one-hot encoding. We first determined the maximum number of aspects in one sentence among the whole dataset and created an array of this length, containing only zeros. For each sentence ...

<s>All parameter combinations were first run using only the sentences as inputs, then using word vectors, then using word vectors and POS tags, and then using word vectors, POS tags and word dependencies. This was done for each of the six recurrent and feed-forward networks, resulting on a total of 24 training and eval...

<s>development using RNN Fig. 16 Aspect sentiment prediction F1 score development using FF-ANN The most notable curves in the sentiment networks were the text-only training processes, since their accuracy increased drastically right from the start. It was very hard to deduce whether it was due to very good training or ...

<s>SemEval and Amazon Datasets PAGE 30 OF 37 5.1.2.3. With Word Vectors and POS Tags The third experiment used the POS tags, along with word vectors applied on the sentence tokens. Since POS tags in contain semantic information which is similar, but not as detailed as the one contained in word dependencies, they were t...

<s>word vectors, P = POS tags, WD = word dependencies) Fig. 17 F1 score comparison of all experiments As expected, the use of word vectors, which is already common practice in many NLP tasks, increased the accuracy compared to only using the input sentences alone in all the implemented neural networks. Moreover, the re...

<s>SemEval and Amazon Datasets PAGE 34 OF 37 Fig. 18 Aspect sentiment prediction F1 score development and trendline using FF-ANN and POS tags 7. Conclusion and Future Work In this work, we have shown that our proposed approach of improving the performance of neural networks for aspect-based sentiment analysis by using ...

<s>THESIS REPORT Tamanna Hasib, Saima Ahmed Rahin Aspect-Based Sentiment Analysis Using SemEval and Amazon Datasets PAGE 36 OF 37 [14] M. Baroni, G. Dinu, G. Kruszewski (2014). Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors. Center for Mind/Brain Sciences, Univ...

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<s>Sequence_105_ID4459.pdfProceedings of the 2019 5th International Conference on Advances in Electrical Engineering (ICAEE), 26-28September, Dhaka, BangladeshA Computational Approach of Recognizing Emotion from Bengali TextsHasan Abid Ruposh and Mohammed Moshiul HoqueDept. of Computer Science & Engineering, Chittagong...

<s>usable compu-tational system is developed that can recognize emotionsfrom Bengali texts. A significant research activities havebeen carried out on emotion recognition in text, especially,in English and European languages. However, a very fewwork are done on sentiment analysis in Bengali text [4][5].In addition, ther...

<s>is done on Bengali blogs and Newstexts at word and sentence level.Most of the previous studies focused on the sentimentdetection in terms of positive, negative or neutral categoriesfrom Bengali blogs, tweets or social media texts. In theproposed approach, our main task is to recognize the basicsix emotions such as h...

<s>test sample islabeled 0, 1, 2, 3, 4, or 5 [depending on the categories].It represents the semantic interpretation of text in the testsample. If the sample text is not labeled within the categoriesthen the system will fails to process it. Fig. 5 shows a sampleoutput of the predicted level of text sample.C. Classifier...

<s>of developed corpus.TABLE ISUMMARY OF THE BENGALI EMOTION CORPUSNumber of documents 1200Number of sentences 3600Number of words 12000Total unique words 21371) Evaluation Measures: In order to evaluate our pro-posed system, we used several evaluation matrices suchas confusion matrix, precision, recall, F1 score and R...

<s>0.63Anger 0.40 0.67Fear 0.69 0.80Surprise 0.72 0.75Disgust 0.71 0.76V. CONCLUSIONThe main purpose of the proposed system is to classify theBengali texts in terms of six basic emotions such as sadness,happiness, fear, anger, disgust, and surprise respectively. Forthis purpose, we developed a emotion corpus of Bengali...

<s>Proc. of Int. Conf. on Advances in SocialNetworks Analysis and Mining. IEEE, 2011, pp. 587–592.[18] D. Dipankar and S. Bandyopadhyay, “Analyzing emotion in blog andnews at word and sentence level,” in Proc. of the 4th Indian Int. Conf.on Artificial Intelligence, 2009, pp. 1402–1414.[19] S. Sriram, , and X. Yuan, “An...

<s>Data Set For Sentiment Analysis On Bengali News Comments And Its Baseline EvaluationInternational Conference on Bangla Speech and Language Processing(ICBSLP), 27-28 September, 2019Data Set For Sentiment Analysis On Bengali NewsComments And Its Baseline EvaluationMd. Akhter-Uz-Zaman AshikDepartment of CSEShahjalal Un...

<s>opinion, that person’sopinion is not judged whether s/he is right or not. So wedecided to create a data set where the data set will not belabeled according to just one person’s opinion to ensure thecredibility of the data set.II. RELATED WORKSentiment Analysis means the characterization of the sen-timent content of ...

<s>Data AnnotationWe wanted to create our own data set with proper attentiongiven to the credibility of the sentiment behind the sentence.When a sentence is labelled, a single tag cannot ensure theactual sentiment of the sentence. A sentence might seemnegative to an individual but might not be for another indi-vidual. ...

<s>the data-set the appropriate metric shouldbe chosen. Four effective measures have been selected for thestudy based on the confusion matrix of the output. These are:Precision(P ) = TP/(TP + FP ) (1)Recall(R) = TP/(TP + FN) (2)Accuracy(A) = (TP +TN)/(TP +TN +FP +FN) (3)F1− Score(F1) = 2.(P.R)/(P +R) (4)Authorized lice...

<s>class of DeepNeural Networks. CNNs are multi-layer perceptrons thatare regularized. Multi-layer perceptrons usually have fullyconnected networks, which can cause over fitting. CNNssolve this issue by taking advantage of hierarchical patternin data and assemble more complex patterns using smallerand simpler patterns....

<s>Language Resources andEvaluation (LREC 2016). Portorož, Slovenia: European LanguageResources Association (ELRA), May 2016, pp. 2703–2709. [Online].Available: https://www.aclweb.org/anthology/L16-1429[5] A. K. Paul and P. C. Shill, “Sentiment mining from bangla datausing mutual information,” in 2016 2nd Internationa...

<s>Sentiment Analysis on Bangladesh Cricket with Support Vector MachineSee discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/329395265Sentiment Analysis on Bangladesh Cricket with Support Vector MachineConference Paper · September 2018DOI: 10.1109/ICBSLP.2018.8554...

<s>polarity of texts as positive, negativeor neutral. And then we shifted our work to detect praise,criticism and sadness. At first we have created a Googleforms and labeled the data with these three classes. Then wepreprocessed and extracted our data as features using TF-IDFand applied machine learning models. For cla...

<s>theseliterature, we have learned the Bengali language Processing.In addition, [11] focused on twitter micro blogging dataand classified positive, negative and neutral from the data.And in [6], they focused on restaurants reviews and classifiedthe polarity of the text. [13] applied several common ma-chine learning te...

<s>stopwords ofso, in, they, but, or all of these as we do not need these wordswhile training our model. In addition, we have manually listedan array for punctuations and Bengali numbers. All of theseunnecessary list of words, numbers and punctuation marksare filtered in the initialization of TF-IDF vectorizer whichis ...

<s>probabilistic model-basedapproach, we have used Multinomial Naive Bayes classifiersto compare and analyze our results.We have chosen 10% data as our random test sets. Then wehave trained our machine learning model with the rest 90%of the dataset. The trained model predicts from the test setswhether a public opinion ...

<s>course we will apply deeplearning theory for our existing system. The most importantpart is that we are working on Bengali language where wehave not done the stemming, spell-checking and Bengali parts-of-speech tagging for our current research. Use of the propernatural language processing will highly improve our sys...

<s>detection ofreviews. Expert Systems with Applications, 36(7):10760–10773, 2009.View publication statsView publication statshttps://www.researchgate.net/publication/329395265</s>

<s>Performance Measurement of Multiple Supervised Learning Algorithms for Bengali News Headline Sentiment ClassificationSee discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/342226447Performance Measurement of Multiple Supervised Learning Algorithms forBengali New...

<s>both combinations where the label and unlabeled data have in mixed.Peoples express their opinion after reading any kinds of text and given the opinion will be negative, positive or neutral. Sentiment analysis helps to appreciate the opinion of providing text documents. News headline is a short text which contains th...

<s>correct prediction result.III. MethodologyMachine learning approaches help to solve NLP problems. In natural language processing important problem such as text analysis, sentiment analysis, speech to text conversion, text summarization, image to text conversion, language to language translation all is solved using m...

<s>length of negative news headline is 12 and the amount of headlines is 2. Minimum length of negative news headline is 2 and the number of headlines is 3. For positive news maximum number of headlines, text length is 14 and the total number of headline 12 where the minimum number of text length is 1 and the amount of ...

<s>means positive headline. Dataset properties resemble given below. a. Total data 1619 b. 11 types of news c. 1109 positive headline and 510 negative headline d. Minimum & maximum word length 1 and 14. Figure 2: Frequency of positive & negative sentiment In figure 2, x-axis contains the frequency of the negative and p...

<s>the headline. Headline sentiment divides two types where 0 means negative headline and 1 means positive headline. Dataset properties resemble given below. a. Total data 1619 b. 11 types of news c. 1109 positive headline and 510 negative headline d. Minimum & maximum word length 1 and 14. Figure 2: Frequency of posit...

<s>machine can’t understand a rare character or word. So in the pre-processing step remove unwanted characters is very important. In Bengali text whitespace, punctuation, some digits are included in unwanted characters.C. Vocabulary Count For vocabulary count, we use Count Vectorizer. It counts the split word which is ...

<s>technique. Because in the dataset label and input-output given. Then define test dataset to remove the unbiased assessment. In the model train, almost 85%data was given and for test dataset, 15% data with 101 random state are defined. E. Machine Learning Algorithms Supervised learning algorithms are used to solve al...

<s>algorithms with a suitable parameter. Briefly discussed in below about uses algorithms.1) Naive Bayes Classifier This algorithm is used to calculate the probability of the classification problem. In our research, we use multinomial NB which is a distinct classifier used for multinomial disposal. Suppose the probabil...

<s>a suitable parameter. Briefly discussed in below about uses algorithms. i. Naive Bayes Classifier This algorithm is used to calculate the probability of the classification problem. In our research, we use multinomial NB which is a distinct classifier used for multinomial disposal. Suppose the probability of the inpu...

<s>of this contraction was added to the dataset text. ii. Stop word remove: In preprocessing removing stop word is very important. Stop word contains the most common word in a text or document. So in natural language processing stop, words are removed from the text for any language modelling. There are many stop words ...

<s>Decision tree for news sentiment. iv. Nearest Neighbors classifier KNN is a non-parametric approach for classification algorithms. Output value calculated by the value of k which means the nearest value of k. where k is a parameter for find related output. k search the closest values for the providing parameter from...

<s>learning algorithms are used to solve all classification problem. The classification problems are following true and false logic. If the predicted input is positive it's true otherwise it’s false. All of the predicted output is depending on the input label. Suppose x is an input variable and y is an output variable....

<s>Trends, 22nd–23rd November, 2019 College of Computing Sciences & Information Technology, Teerthanker Mahaveer University, Moradabad, India238 Copyright © IEEE–2019 ISBN: 978-1-7281-3245-7parameter from the dataset. In our experiment, we use the value k=3 and provide a good result. Each instant is selected by the dis...

<s>type of problem. The hyperplane is used in each support vector machine classifier. Each hyperplane divided each dataset into two-part. The hyperplane is worked based on the kernel where the kernel represents some algebraic calculation. We use SVC kernel for our classification problem. SVC contain a vector classifier...

<s>0.39 69% 1 0.76 0.82 0.79 Decision Tree 0 0.33 0.40 0.36 60% 1 0.73 0.67 0.70 KNN 0 0.45 0.39 0.41 68% 1 0.76 0.79 0.78 Fig. 5: Accuracy Chart for ML AlgorithmsNow we have used another table to check the classification result with a Bangla News headline. Where all of that applied algorithm predicts the accurate outp...

<s>of San Francisco, 2018.[4] Zhang, Wenbin, and Steven Skiena. “Trading strategies to exploit blog and news sentiment.” In Fourth international aAAI conference on weblogs and social media. 2010.[5] Fu Y, Hao JX, Li X, Hsu CH. Predictive Accuracy of Sentiment Analytics for Tourism: A Metalearning Perspective on Chinese...

<s>Analyzing Performance of Different Machine Learning Approaches With Doc2vec for Classifying Sentiment of Bengali Natural LanguageSee discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/332582518Analyzing Performance of Different Machine Learning Approaches WithDo...

<s>interact using Facebook in Bangladesh[4]. A very large number of data has been comprised overthe Internet as a result of enormous dealing with socialmedia platforms which conveys a significant contribution inSentiment analysis (SA) [1]. To be specific, analyzing thereactions by users accumulated from social media co...

<s>or negative review [8]–[10]. Turney et al. used simpleunsupervised learning algorithm which finds average semanticorientation of the phrases form the review containing adjectivesor adverbs [8]. In system [9] Dave et al. trained a classifierusing a self-tagged corpus of reviews form web sites. Pang etal. applied mach...

<s>among “Like” and otherreactions can be expressed as-• Strongly positive correlation with “Love” and “Wow”.• Weakly positive correlation with “Sad” and “Angry”.Although “Like” reaction is the most common, we haveconsidered this as low-effort data from users and ignored itwhile classifying the sentiment polarity of a ...

<s>is represented as a vector where similardocuments have closer values. We used our corpus, preparedusing the process described in the previous subsection to traindoc2vec model. All the labeled sentences from our corpuswere fed into doc2vec model to build its vocabulary. Here eachlabeled sentence contains a list of Be...

<s>achieve more accuracy as therates of accuracy in both training and validation datasets areincreasing significantly over model training epochs. On theother hand, from the plot of loss on both the training and thevalidation datasets represented in Fig. 3 indicates the sign forstopping model training in earlier epoch i...

<s>of the40th Annual Meeting on Association for Computational Linguistics -ACL ’02, pp. 417–424, 2002.[9] K. Dave, S. Lawrence, and D. M. Pennock, “Mining the Peanut Gallery:Opinion Extraction and Semantic Classification of Product Reviews,”in Proceedings of the 12th international conference on World Wide Web(WWW ’03),...

<s>Sentimental Style Transfer in Text with Multigenerative Variational Auto-EncoderInternational Conference on Bangla Speech and Language Processing(ICBSLP), 27-28 September, 2019Sentimental Style Transfer in Text withMultigenerative Variational Auto-EncoderMehedi Hasan PalashDepartment ofComputer Science and Engineeri...

<s>betterresult, for this expands the window of opportunity for gettingmore diverse outputs. Therefore we get more relevant outputs.We use two different decoders to get two types of response.One for positive and another for negative styled output.II. RELATED WORKStyle transfer with non parallel text has been exercisede...

<s>get 2500 negative comments and 2500 positivecomments.B. PreprocessingWhen we work with text data, we have to read stream ofcharacters. Single characters normally don’t mean anything.If we combine them together carefully, they will make sense.Tokenizing means split the stream of characters such that wecan consider th...

<s>positive sentence is small, and vice versa.Fig. 4. Comparing our results with state of the art modelsComparing with the human tested results with theyelp dataset, all the parameters(grammar, context, positiv-ity/negativity) are much higher than all the state of the artmodels. This means that machines couldn’t be abl...

<s>will have many interesting applica-tions, like-personalized chatbots and even making an universallanguage translator.VIII. ACKNOWLEDGEMENTWe would like to thank the NLP group of Shahjalal Univer-sity of Science and Technology for the necessary insight andexpertise that greatly assisted the research.REFERENCES[1] A. ...

<s>dataData DescriptorDatasets for Aspect-Based Sentiment Analysis inBangla and Its Baseline EvaluationMd. Atikur Rahman * and Emon Kumar Dey *Institute of Information Technology, University of Dhaka, Dhaka 1000, Bangladesh* Correspondence: bsse0521@iit.du.ac.bd (M.A.R.); emonkd@iit.du.ac.bd (E.K.D.)Received: 20 March ...

<s>review of a restaurant revealstwo aspects: service and food. Both aspects have a positive polarity.“The service was excellent and the food was delicious.”As one can see, the name of the aspect categories are explicitly mentioned in this review. A reviewmight also contain implicit categories; for example, “The staff ...

<s>In [5], the author created an ITproduct-review dataset for the ABSA task, in which a total 2200 reviews were contained.The contribution of this paper is as follows:• We have collected and presented two Bangla datasets for ABSA and have made thempublicly available.• We performed statistical linguistic analysis on the...

<s>pages (https://www.facebook.com/BBCBengaliService/; https://www.facebook.com/DailyProthomAlo/). Some comments are collected from two popular Bengali Websites, BBC Bangla (http://www.bbc.com/bengali), and the Daily Prothom Alo (http://www.prothomalo.com). This dataset was collected by the authors of this paper. The c...

<s>in English and also wrote Bangla sentences written in the English alphabet. We did notconsider these opinions in our dataset. In addition, some comments had only emoticons and noother text or words. We also omitted these for our dataset. All of the processes were done manuallyby the authors. The following section de...

<s>define the category and polarity. Comment: এই িপেচ রান করা টাফ, বািলং িনঃসে েহ ভােলা হেয়েছ Participant Voting for Category Voting for Polarity P1 Bowling Positive P2 Bowling Positive P3 Batting Negative P4 Batting Negative P5 Other Neutral P6 Bowling Positive P7 Other Neutral P8 Bowling Positive P9 Bowling Positive ...

<s>the categories with their polarity in our dataset. Table 4 shows an example for this scenario. Participant Voting for Category Voting for PolarityP1 Bowling PositiveP2 Bowling PositiveP3 Batting NegativeP4 Batting NegativeP5 Other NeutralP6 Bowling PositiveP7 Other NeutralP8 Bowling PositiveP9 Bowling PositiveP10 Ba...

<s>NegativeP2 Team NegativeP3 Batting NegativeP4 Batting NegativeP5 Batting NegativeP6 Team NegativeP7 Team NegativeP8 Batting NegativeP9 Team NegativeP10 Team NegativeWe can see from Table 4 that 50% of the evaluators voted for Batting, and 50% of the votes werefor the Team category, both with a negative polarity. As ...

<s>3034 different comments with five different categories, that is, Batting, Bowling, Team, TeamManagement, and Other. Each of the categories contains three different polarities: positive, negative,and neutral. For example, the Batting category contains a total of 583 comments, for which 138 are ofpositive, 389 are of ...

<s>times as the third most frequent, and so on. Figure 1 shows the diagram in which we plotted the words of our Cricket dataset. The plot follows the trend of Zipf’s law. We also calculated the reliability of the annotation process. The value of the intraclass correlation (ICC) was 0.71. Table 7. The complete statistic...

<s>xlsx format. খুব সীিমত আসন আেছ eবং খাদয্ পাoয়ার জনয্ যেথ aেপkা করেত হেব। ambience negative খুব সীিমত আসন আেছ eবং খাদয্ পাoয়ার জনয্ যেথ aেপkা করেত হেব। service negative দাম তুলনামূলকভােব কম। price positive াi িছল মজাদার food positive যিদo খাবারিট চমৎকার িছল, eিট সsা িছল না। food positive যিদo খাবারিট চমৎকার িছল, eিট ...

<s>this paper, we experimented with the first subtask, that is, the extraction ofthe aspect category. We applied three major steps to extract the aspect category. Firstly, preprocessingwas performed on the dataset. After this, we extracted features from the data and finally performedclassification using some popular cl...