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<s>authors declare no conflict of interest. References 1. Trusov, M.; Bucklin, R.E.; Pauwels, K. Effects of word-of-mouth versus traditional marketing: Findings from an internet social networking site. J. Mark. 2009, 73, 90–102. 2. Jeyapriya, A.; Selvi, C.K. Extracting Aspects and Mining Opinions in Product Reviews Usi... |
<s>K. Czech Aspect-Based Sentiment Analysis: A New Dataset and PreliminaryResults. Available online: https://pdfs.semanticscholar.org/cbd8/7f4201c427db33783b1890bca65f5bf99d2c.pdf(accessed on 3 May 2018).6. Apidianaki, M.; Tannier, X.; Richart, C. Datasets for Aspect-Based Sentiment Analysis in French. Availableonline:... |
<s>Establishing a Formal Benchmarking Process for Sentiment Analysis for the Bangla LanguageA K M Shahariar Azad Rabby 1, Aminul Islam1 and Fuad Rahman21 Apurba Technologies, Dhaka, Bangladesh2 Apurba Technologies, Sunnyvale, CA, USArabby@apurbatech.com, aminul@apurbatech.com, fuad@apurbatech.comAbstract. Tracking sent... |
<s>of sentiment analysis in Bangla—which as stated already is not very rich—but the principal issue that becomes crystal clear is that whatever efforts have been reported on this topic, it is absolutely impossible to compare them since they use different datasets and almost always the datasets reported are not availabl... |
<s>Sentiment extraction of words Support Vector Machine(SVM) and Maximum Entropy (MaxEnt). Support Vector Machine, Logistic Regression, etc. LSTM, using two types of loss functions – binary cross-entropy and categorical cross-entropy Word embedding methods Word2vec Skip-Gram and Continuous Bag of Words with an addition... |
<s>available Size 1500 short Bangla comment 9,500 comments 201 Comments 45,000 9000 words 1000 restaurant reviews Dataset Collected from various social sites Collected from different source Collected from YouTube Collected from Facebook using Facebook graph api Collected from Facebook Group Self-collected Method Used T... |
<s>Dataset was collected from a popular online news portal “Prothom Alo” (প্রথম আলো), tagged manually and checked twice for validation. Also, the dataset is open-source for all types of non-commercial usage, intended for educational and research use. The other two datasets can easily be obtained from GitHub. We also me... |
<s>performance among them. We used K-fold cross-validation and Grid Search to find the best parameters for all of our algorithms.4.1 Multinomial Naive BayesMultinomial Naive Bayes estimates the conditional probability of a particular word given a class as the relative frequency of term t in samples belonging to class c... |
<s>uses a gradient boosting framework. XGBoost Gradients are fantastic models because they can increase accuracy over a traditional statistical or conditional model and can apply themselves quite well to the two primary types of targets.4.9 LSTMLong Short-Term Memory (LSTM) networks are a modified version of recurrent ... |
<s>65.56% 65.42% 67.16% 63.77% ABSA Sports [ 47, 63][ 41, 393] 80.88% 73.30 88.31% 86.18% 90.55% ABSA Restaurant [240, 22][ 75, 25] 73.20% 70.00% 34.01% 53.19% 25% All Dataset [629, 403][387, 945] 66.58% 71.36% 70.52% 70.10% 70.94%Table 9. Sensitivity Analysis of Random Forest Dataset TPR TNR FNR FPR PPV NPV FDR FOR Ap... |
<s>ABSA Restaurant [236, 26][ 77, 23] 73.20% 69.38% All Dataset [500, 532][368, 964] 65.44% 70.44%Table 17. Sensitivity Analysis of ADA Boost Dataset TPR TNR FNR FPR PPV NPV FDR FOR Apurba 82.77 38.12 17.23 61.88 65.31 61.11 34.69 38.89 73.01 ABSA Sports 96.77 11.82 3.23 88.18 81.24 48.15 18.76 51.85 88.33 ABSA Restaur... |
<s>Acc Restaurant Acc All Data LSTM 69.52% 79.77% 72.38% 73.18% XGBoost 68.81% 79.60% 76.52% 69.25% Multinomial Naive Bayes 68.54% 76.65% 75.42% 68.82% Logistic Regression 67.72% 80.33% 74.86% 68.61% Bernoulli Naive Bayes 69.16% 80.33% 71.82% 67.98% SVM 66.83% 70.77% 69.89% 67.94% Random Forest 60.59% 80.88% 73.20% 66.... |
<s>publicly available dataset means that every researcher has to first collect and label the data before any training can take place. And since each new algorithm is evaluated on a different dataset, it is also virtually impossible to compare the different approaches in terms of their accuracy and quality. We hope that... |
<s>on Informatics, Electronics & Vision (ICIEV) (pp. 1-6). IEEE.Hossain, M. S., Jui, I. J., & Suzana, A. Z. (2017). Sentiment analysis for Bengali newspaper headlines (Doctoral dissertation, BRAC University).Hassan, A., Amin, M. R., Mohammed, N., & Azad, A. K. A. (2016). Sentiment analysis on Bangla and Romanized Bangl... |
<s>September). Sentiment analysis on Facebook group using lexicon-based approach. In 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT) (pp. 1-4). IEEE.Sharif, O., Hoque, M. M., & Hossain, E. (2019, May). Sentiment Analysis of Bengali Texts on Online Restauran... |
<s>Retrieving YouTube Video by Sentiment analysis on User CommentSee discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/325119126Retrieving YouTube Video by Sentiment Analysis on User CommentConference Paper · May 2018DOI: 10.1109/ICSIPA.2017.8120658CITATIONSREADS6... |
<s>with the other community members (via the comments feature) [3]. These activities (like / dislike /number 1 https://www.youtube.com of views) of the users can serve as a global indicator of quality or popularity for a particular video [3, 4]. Moreover, these Meta data (like/dislike/number of views) serve the purpose... |
<s>make a decision (comment rating, topic categories etc.,) about the particular video [6]. These comments are also used to annotate the video object [7, 9]. Comments also reflect the user’s behavior and could use to find the troll users [1]. Moreover, by analyzing the sentiment of comments it is possible to find the u... |
<s>Both are made from the processed comments text. To make datasets first, in the processed text MySQL stop word is applied to remove all the stop words and then convert all the words into their singular form and thus make dataset 1. Next, for Dataset 2 all the adjectives [14] (important words of the comment text) of t... |
<s>the experiment results of the proposed sentiment analysis approach. To evaluate the proposed approach the experiment is conducted on 1000 videos of YouTube which were selected randomly. However, precisely 10 categories (education, science and technology, entertainment, cartoon, etc.) were selected for those videos. ... |
<s>was essential. From the result in Table III, it seems that first video of dataset 1 gives better accuracy than second video of dataset 1. However, accuracy of dataset 2 of second video is much better than dataset 2 of first video. Therefore, in such case considering both dataset gives us the real picture of those vi... |
<s>YouTube,” International Journal on Digital Libraries, 16(2), 2015, pp.161-179. [10] H. Lee, Y. Han, Y. Kim and K. Kim, “Sentiment analysis on online social network using probability Model,” In Proceedings of the Sixth International Conference on Advances in Future Internet, 2014, pp. 14-19. [11] K. Filippova and K. ... |
<s>/CGTimes-BoldItalic /CGTimes-Italic /CGTimes-Regular /CharterBT-Bold /CharterBT-BoldItalic /CharterBT-Italic /CharterBT-Roman /CheltenhamITCbyBT-Bold /CheltenhamITCbyBT-BoldItalic /CheltenhamITCbyBT-Book /CheltenhamITCbyBT-BookItalic /Chiller-Regular /Cmb10 /CMB10 /Cmbsy10 /CMBSY10 /CMBSY5 /CMBSY6 /CMBSY7 /CMBSY8 /C... |
<s>/KrutiDev060Condensed /KrutiDev060Italic /KrutiDev060Thin /KrutiDev060Wide /KrutiDev070 /KrutiDev070Condensed /KrutiDev070Italic /KrutiDev070Thin /KrutiDev070Wide /KrutiDev080 /KrutiDev080Condensed /KrutiDev080Italic /KrutiDev080Wide /KrutiDev090 /KrutiDev090Bold /KrutiDev090BoldItalic /KrutiDev090Condensed /KrutiDe... |
<s>/EncodeColorImages true /ColorImageFilter /DCTEncode /AutoFilterColorImages true /ColorImageAutoFilterStrategy /JPEG /ColorACSImageDict << /QFactor 0.76 /HSamples [2 1 1 2] /VSamples [2 1 1 2] /ColorImageDict << /QFactor 0.76 /HSamples [2 1 1 2] /VSamples [2 1 1 2] /JPEG2000ColorACSImageDict << /TileWidth 256 /TileH... |
<s>Sentiment Extraction From Bangla Text : A Character Level Supervised Recurrent Neural Network ApproachSee discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/327820158Sentiment Extraction From Bangla Text : A Character Level SupervisedRecurrent Neural Network App... |
<s>have a better analytics to generate a more accurateinformation, we need to be able to analyze people reviews.The main contributions of this paper are as follows:• Showing the effect of character-level representation inBangla language.• Making a comparison on traditional representation ofwords with our approach.The p... |
<s>are consideredthose containing english words or only emoticons or onlyrandomized bangla words which are not necessary for ourclassification.C. Character EncodingFor using this data to our model, we first representedthis dataset in a vector space. There are different methodsto represent text data. Tf-Idf, Bag of Word... |
<s>study onthree types of RNN and found that Gated Recurrent Unit issuperior then other two. GRU is also computationally moreefficient than LSTM.zt = σ(Wz.[ht−1, xt]) (1)rt = σ(Wr.[ht−1, xt]) (2)h̃t = tanh(W.[rt ∗ ht−1, xt]) (3)ht = (1− zt) ∗ ht−1 + zt ∗ h̃t (4)Here are the equations that demonstrates how the hiddensta... |
<s>keras [14]which is a high-level neural networks API.C. Results and DiscussionThe result that we achieved from the character level modelover word level model is pretty good. We came up with 80%accuracy on character level mode and 77% accuracy from ourbaseline model with word level representation. Over the recenttime,... |
<s>Chowdhury, Shaika, and Wasifa Chowdhury. ”Performing sentimentanalysis in Bangla microblog posts.” Informatics, Electronics & Vision(ICIEV), 2014 International Conference on. IEEE, 2014.[3] Das, Amitava, and Sivaji Bandyopadhyay. ”Phrase-level polarity identifi-cation for Bangla.” Int. J. Comput. Linguist. Appl.(IJC... |
<s>See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/341384787ASPECT BASED SENTIMENT ANALYSIS IN BANGLA DATASET BASED ONASPECT TERM EXTRACTIONConference Paper · May 2020CITATIONREADS2249 authors, including:Some of the authors of this publication are also worki... |
<s>of customers from var-ious sources like social media, online reviews or customer’s online surveys.e.g. “Food is decent but service is so bad”, it is evident that the sentiment towards food is positive however contains a powerful negative sentiment towards facet service. So, after classi-fying the overall sentiment, ... |
<s>as a framework titled “aspect-based sentiment analysis” [26] address the problem of getting only the overall sentiment from a sentence where aspect refers to a component or attribute of an entity. One of the first studies for both explicit and implicit aspects extraction from product reviews, proposed a rule-based a... |
<s>remains un-explored due to very less availability and lack of various re-sources and tools such as annotated corpora, lexicons, Part-of-Speech (PoS) tagger etc. that plays vital role while performing ABSA. Therefore, the concentration of this paper is to use the annotated dataset from [13] and perform ABSA’s aspect ... |
<s>2) Removing punctuations: One of the very popular and often applied preprocessing is removing punctuations. Even the full-stop “.” in Bengali language refers to “|” sign. So, we have removed punctuations. 2.3 Feature Extraction We have represented reviews (texts) into numeric form to use them as features. The proces... |
<s>and text classification tasks [7] [9]. 2.5 Languages and Tools The system is implemented using Python 3(Jupyter NoteBook) in Anaconda. The Python modules skLearn- which provides a set of modules for machine learning and data mining [23] is used. For NLP tasks NLTK (A leading platform for building Python programs and... |
<s>and 27% from KNN resulting higher score than [13], where the previous results for both algorithms were 34% and 25% respectively. However, RF provided same result as [13], which is 37%. Table 5. Model Performance Comparison of cricket dataset Model Precision (Previ-ous re-sult) Precision (Our re-sult) Recall (Previou... |
<s>Katerina Veselovská. (2015) Czech aspect-based sentiment anal-ysis: A new dataset and preliminary results. In: ITAT 2015 [2] Mohammad Al-Smadi, Omar Qawasmeh, Bashar Talafha, Muhannad Quwaider (2015) Human annotated arabic dataset of book reviews for aspect-based sentiment analysis. In: 3rd Interna-tional Conference... |
<s>Semantic Evaluation (SemEval 2014), Association for Computa-tional Linguistics, Dublin Ireland, pp 27-35. DOI: 10.3115/v1/S14-2004 [21] ScikitLearn. (n.d.). sklearn.feature_extraction.text.TfidfVectorizer¶. Retrieved May 2019, from scikit learn: https://scikit-learn.org/stable/modules/generated/sklearn.feature_extra... |
<s>An Annotated Bangla Sentiment Analysis CorpusInternational Conference on Bangla Speech and Language Processing (ICBSLP), 27-28 September 2019 978-1-7281-5242-4/19 ©2019 IEEE An Annotated Bangla Sentiment Analysis Corpus Fuad Rahman Apurba Technologies Ltd. Dhaka, Bangladesh fuad@apurbatech.com Mahfuza Begum Apurba T... |
<s>Although there are some existing data set for sentiment analysis, but most of these are not available publicly. Some publicly available data set are small, e.g. [4] has about 4,000 sentences, whereas [1] has about 7,000 sentences. One of the most significant resources is described in [6]. This corpus size is around ... |
<s>a root or not and manually corrected the roots for those words that were stemmed wrongly. Once a clean word list was created, we then tagged the polarity of each word manually, using the same three-tiered approach as described before. This step also resulted in identifying some words that were ambiguous. These are t... |
<s>Language Processing (ICBSLP), 27-28 September 2019 Fig. 9 shows the frequency of the top 20 words in our corpus. Fig. 10 Word frequency of top 20 positive words Fig. 10 shows the frequency of the top 20 positive words in our corpus. Fig. 11 Word frequency of top 20 negative words Fig. 11 shows the frequency of the t... |
<s>Bengali WordNet Affect for Analyzing Emotion. Int. Conf. on the Computer. Processing of Oriental Languages, pp. 35-40, 2010. [8] Cliff Goddard. Natural Language Processing, Edition: 2nd edition, Chapter: 5, Publisher: CRC Press, Taylor & Francis, Editors: Nitin Indurkhya, Fred J. Damerau, pp.92-120 [9] Mohammad Dani... |
<s>See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/336853602Depression Analysis of Bangla Social Media Data using Gated Recurrent NeuralNetworkConference Paper · May 2019DOI: 10.1109/ICASERT.2019.8934455CITATIONSREADS3 authors, including:Some of the authors ... |
<s>and analyze depression and causes behind it.Micro-blogging sites like Facebook, Twitter, LinkedIn arenow more popular than ever before. On these sites, peoplefrequently share their daily activities and emotional reac-tions. Wang et al., used Sina, a Chinese micro-bloggingwebsite, to collect data and applied psycholo... |
<s>approach.The rest of this paper is arranged as follows. In Section II,we have discussed some relevant existing research works,our proposed method is described in Section III, andin section IV, the results of our method and relevantdiscussions are presented. Finally, we conclude our workin Section V along with some f... |
<s>Neural Network (DNN) selection. Theysystematically compared CNN, LSTM, and GRU models.It covered a broad range of NLP tasks, such as sentimentclassification, relation classification, textual entailment,answer selection, question relation match, path query an-swering, part-of-speech tagging, etc. For their experiment... |
<s>neural network. Therefore, we stratified ourdataset to reduce the effect of its small size while trainingthe GRU model. In this step, data were rearranged into aone-to-one approach, that is, one depressive data followedby one non-depressive data, and continuously repeatedthis process all over the balanced dataset. A... |
<s>highest accuracy for tuning batchsize with no. of epochs. Effects for tuning batch size withcorresponding no. of epochs are shown in Fig 1 (impl 6to 11). It indicates how accuracy changes with differentbatch size and no. of epochs.c) Tuning No. of GRU layers with No. of Epochs: Inthe final step of Hyper-parameter tu... |
<s>size to 512, batch size to 5, and tuned no. ofGRU layers along with no. of epochs. Our GRU modelaccomplished 74.8% accuracy with 5 layers over 3 epochs,69.6% accuracy with 10 layers over 3 epochs, and 56.5%accuracy for 5 layers over 10 epochs. These results ledus to the decision that large no. of GRU layers does not... |
<s>C. Zhang, Y. Ji, L. Sun, L. Wu, and Z. Bao,“A depression detection model based on sentimentanalysis in micro-blog social network,” in Pacific-Asia Conference on Knowledge Discovery and DataMining, pp. 201–213, Springer, 2013.[3] “Languages of the World,” Ethnologue. [Online].Available: https://www.ethnologue.com/. [... |
<s>Public Sentiment Analysis Basedon Social Media Reactions forBangla Natural LanguageMd. Tazimul HoqueStudent Id: 012161021Department of Computer Science and EngineeringUnited International UniversityA thesis submitted for the degree ofM.Sc. in Computer Science & EngineeringJune 2020© Md. Tazimul Hoque, 2020Approval C... |
<s>well as bandwidth. This relatively newerdoc2vec technology has not yet been implemented for Bengali sentiment analysis and itsfeasibility is also unknown. In this study, we have trained doc2vec and word2vec modelsusing a corpus constructed with 10500 Bengali documents. The corpus consists of threetypes of data diffe... |
<s>. . . . . . . . . . . . . . . 42.2 Natural Language Processing . . . . . . . . . . . . . . . . . . . . . . . . . 52.3 Sentiment Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.3.1 Different Levels of Sentiment Analysis . . . . . . . . . . . . . . . . 62.3.1.1 Document level . . . . . . . . ... |
<s>. . . . . 112.6.2 Unsupervised Machine Learning . . . . . . . . . . . . . . . . . . . 112.6.3 Semi-supervised Machine Learning . . . . . . . . . . . . . . . . . . 112.6.4 Reinforcement Machine Learning . . . . . . . . . . . . . . . . . . . 122.7 Machine Learning Tools for Classification . . . . . . . . . . . . . . .... |
<s>. . . . . . . . . . . . . . . . . . . . . 192.8.6 Macro Average for Precision, Recall and F1-score . . . . . . . . . . 202.8.7 k-Fold Cross Validation . . . . . . . . . . . . . . . . . . . . . . . . 202.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Proposed Method 223.1 Overvi... |
<s>. . . . . . . . 284.1.2.2 Doc2vec Model . . . . . . . . . . . . . . . . . . . . . . . . 304.1.3 Classifier Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324.1.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344.2 Result and Analysis . . . . . . . . . . . . . . . . . . . . .... |
<s>model with tag vector [3] . . . . . . . . . . . . . . . . . . . . . . . 112.5 LSTM block containing input, output and forget gates [4] . . . . . . . . . 172.6 BLSTM classifier design [5] . . . . . . . . . . . . . . . . . . . . . . . . . . 173.1 Proposed architecture of our research work . . . . . . . . . . . . . . .... |
<s>analysis is also known as opinion mining, mood or emotion analysis which is awell-known part of Natural Language processing (NLP). The year 2001 or around can bemarked as the beginning of the research awareness in the field of SA and opinion mining[10]. Research papers mentioning “sentiment analysis” focus specifica... |
<s>build classification models so that they can classifythe sentiments from users’ reactions in different posts published in Bengali.Our work can be defined into the following phases:• Corpus construction phase: This phase includes corpus collection, filtering, label-ing raw corpus according to sentiment score.• Model ... |
<s>70.04% and a recall of 63.02%. Amin et al. used ”word2vec” model for vector rep-resentation of Bengali words [21]. They achieved 75.5% of accuracy using ”word2vec”word co-occurrence score with the words sentiment polarity score. They collected 16,000Bengali single line and multiline comments from blog posts and tagg... |
<s>like. Entity level sentiment analysis looks directly at option itselfinstead of looking at language construction like - documents, paragraphs, sentences,clauses or phrases. Here the main idea is an opinion consists of a sentiment (emotion)and a target (of that sentiment). Opinion without any target being identified ... |
<s>“a”,“very”, “boy”.• Continuous Bag of Words (CBOW) : In converse, CBOW can predict a wordgiven surrounding words. The center word vector is generated by the sum ofcontext words.For their classification algorithm both methods use artificial neural networks. Initiallyeach word is assigned to a random N- dimensional ve... |
<s>operate. After analyzestraining data-set, supervised learning algorithm produces an inferred function, whichcan be used for mapping new inputs.2.6.2 Unsupervised Machine LearningIn unsupervised machine learning, system is trained with unlabeled data. The systemwill be able to classify new inputs after it learns patt... |
<s>leads us to linear decision surfaces, which can be determined bycomparing log-probability ratios log[P (y = k|X)/P (y = l|X)]:logP (y = k|X)P (y = l|X)= logP (X|y = k)P (y = k)P (X|y = l)P (y = l)= 0⇔(µk − µl)tΣ−1X =(µt−1µk − µt−1µl)− log P (y = k)P (y = l)(2.4)2.7.1.3 Support Vector Machine (SVM)Support Vector Mach... |
<s>y)|xj <= tmQright(θ) = Q \Qleft(θ)(2.9)Using an impurity function H() we can determine the impurity at m, the selectiondepends on the task being performed (either classification or regression)G(Q, θ) =nleftH(Qleft(θ)) +nrightH(Qright(θ)) (2.10)To minimise impurityθ∗ = argminθ G(Q, θ) (2.11)Recurse for subsets Qleft(... |
<s>of the layersit requires, hence the model is ready to use. Sequential model requires prior knowledgeabout its input shape. For this purpose, in the first layer of a Sequential model, itsinput shape information is served. Using compile method, learning process is configuredbefore training a model. Input data and labe... |
<s>which represents totalnumber of groups that a given data-set is to be split into. We can use a specific valuefor the parameter k and then use this number in place of k to refer the cross validation.The working procedure of k-fold cross validation is, it takes a group from k split andhold it as test data-set. Remaini... |
<s>test and evaluate performance ofML classifiers.3.2 Corpus CreationOur corpus creation planning can be divided into three steps - data collection, datafiltering and data labeling. These three steps are described below -Figure 3.1: Proposed architecture of our research work3.2.1 Data CollectionOur primary data source ... |
<s>categorize it based on sentiment polarity. We have considered positive, negativeand neutral sentiment in this work. To construct a corpus for Bengali sentiment analysis,different sources have been considered, among which Facebook post data seems mostpromising for SA as they represent the most natural form of languag... |
<s>3500 Bengali sen-tences. Socian Ltd. [36] provided a public corpus containing 4,000 labeled Bengalisentences according their sentiment polarity, either positive or negative which containsequal distribution of labeled data. They have collected this corpus from different socialmedia platforms, news paper sites and blo... |
<s>-TFIDF (t, d) = TF (t, d) ∗ IDF (t) (4.1)where the IDF is calculated as -IDF (t) = log[DF (t)] + 1 (4.2)Here n is the number of documents in data-set. DF(t) is the document frequency of t,the document frequency is the number of documents in the document set that containthe term t. The effect of adding “1” to the IDF... |
<s>unimportant words whiletraining the model. Below we are representing some example sentences from our data-setand their corresponding document vector from doc2vec.• Positive Bengali Sentence:িতিন লখক িহেসেব পুেরা দেশ িবখ াত হেয় ওেঠনCorresponding document vector (100 dimension) -[ 0.13282606 -0.19305475 0.3983899 ....... |
<s>the effectiveness of our employed ML classifiers, we’ve applied k-fold crossvalidation technique and retained performance evaluation scores - accuracy, F1 score,precision and recall from each cross validation steps. For train and test ML classifiers,we’ve used document vectors gained from doc2vec and TF-IDF averaged... |
<s>57.514.2.1.2 10-Fold Cross Validation - Doc2vecTable-4.3 represents 10-fold accuracy scores with a mean column for doc2vec documentvectors.Table 4.3: 10-fold accuracy scores for doc2vec document vectorsClassifier K1 K2 K3 K4 K5 K6 K7 K8 K9 K10 MeanBLSTM 74.57 77.05 73.71 74 76.67 74.38 74.29 77.05 78.19 75.71 75.56L... |
<s>and document vectors from doc2vec.From this table our observation is, almost all ML classifiers performed slightly betterwith TF-IDF averaged document vectors than doc2vec document vectors. K-Neighborsand DT perform much better with TF-IDF averaged word2vec. Only GaussianNB hasachieved better result with doc2vec mod... |
<s>highly correlated, it performs very poorly. It alsofails to consider word occurrence frequency in feature vector regarding text classificationproblem. In our experiment GaussianNB has achieved accuracy of 61.58% with TF-IDFaveraged document vector using word2vec. But it has achieved good result using doc2vecwhich is... |
<s>on other countries.• Group and Organizational Impact: Religion and politics also have impact onhuman sentiment. Based on peoples believe/group/organization, their sentimenton a topic can vary.Other limitations are related to data collection, filtering and system design.• Data Source Availability: Availability of Ben... |
<s>Classification of Product Reviews. In Proceedingsof the 12th international conference on World Wide Web (WWW ’03), pages 519–528, 2003.[15] Bo Pang, Lillian Lee, Z. A. Bán, Bo Pang, Lillian Lee, and ShivakumarVaithyanathan. Proceedings of the Conference on Empirical Methods in Natu-ral Language Processing. Proceedin... |
<s>2015.https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.htmlhttps://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.htmlAppendix AMy Publications1. Hoque, M. T., Rifat-Ut-Tauwab, M., Kabir, M. F., Sarker, F., Huda, M. N., andAbdullah-Al-Mamun, ... |
<s>Topic Modelling and Sentiment Analysis with the Bangla Language: A Deep Learning Approach Combined with the Latent Dirichlet AllocationTopic Modelling and Sentiment Analysis with the Bangla Language: A Deep Learning Approach Combined with the Latent Dirichlet AllocationMustakim Al HelalA thesisSubmitted to the Facul... |
<s>for topic modelling and sentimentanalysis for the Bangla language.AcknowledgementsMy first debt of gratitude goes to my supervisor Dr. Malek Mouhoub, who en-couraged me to follow this path and provided me with constant support during myM.Sc. studies. I would like to express my sincere thanks for his valuable guidanc... |
<s>. . . . 212.2.5 Singular Value Decomposition . . . . . . . . . . . . . . . . . 222.2.6 Evaluation of Topics . . . . . . . . . . . . . . . . . . . . . . 242.2.7 Recurrent Neural Network . . . . . . . . . . . . . . . . . . . 252.2.8 Long Short Term Memory . . . . . . . . . . . . . . . . . . . 262.2.9 Gated Recurrent U... |
<s>. . . . . . . . . . . . . . . . . . . . . . . 484.3 Preprocessing and Cleaning . . . . . . . . . . . . . . . . . . . . . . 484.3.1 Tokenization . . . . . . . . . . . . . . . . . . . . . . . . . . . 494.3.2 Stop Words . . . . . . . . . . . . . . . . . . . . . . . . . . . 494.3.3 Bag of Words Model . . . . . . . . . .... |
<s>. . . . . . . . . . . . . . . . . . . . . . . 312.7 Diagram showing GRU tanh function [32] . . . . . . . . . . . . . . 322.8 Diagram showing GRU function [32] . . . . . . . . . . . . . . . . . 333.1 The dataset for the sentiment analysis work . . . . . . . . . . . . . 363.2 Characters . . . . . . . . . . . . . . . .... |
<s>. . . . 574.9 Coherence based number of topics (t=20) . . . . . . . . . . . . . . . 584.10 Similarity Dissimilarity of Cosine average . . . . . . . . . . . . . . . 664.11 Model performance comparison . . . . . . . . . . . . . . . . . . . . 674.12 Document topic distribution for movie news . . . . . . . . . . . . . 7... |
<s>started with the basic ideas of Natu-ral Language Processing (NLP). The solution of the discussed problem has beendefined. This chapter also talks about the main contribution for this thesis. Theorganization of the thesis will be discussed in section 1.31.1 Problem Statement and MotivationsNatural Language Processin... |
<s>analysis in Bangla has a wide range of future possibilities withdeep learning. Deep learning methods are being applied successfully to naturallanguage processing problems and achieving an acceptable accuracy. RecurrentNeural Network, a variation of Neural Network (NN) is used for processing se-quential data. The cla... |
<s>the background knowledge that is necessary for understanding therest of the thesis work. Basic concepts of the Recurrent Neural Network (RNN)are introduced. Then the LSTM algorithm is discussed in detail. Further, variousconcepts of topic modelling are explained. Text processing requirements are alsodiscussed in thi... |
<s>afterperforming its respective job. Accordingly, in this research the model has differentlayers. First layer of the model is the embedding layer. In this layer word to vectorrepresentation is done. The second layer in their model is the LSTM layer withdimensions of 128 units. Then comes the fully connected third lay... |
<s>in[1], a semi supervised technique has been applied due to the unavailability of alarge amount of labeled data. Self training bootstrapping method is used. First, asmall chunk of the dataset was trained on the basis of the frequency of positive andnegative words. After the model acquires the knowledge based on this ... |
<s>small dataset is used for this research. Some similar research addresseddata from social media [10]. Here, two different approaches were discussed toidentify the polarity of a Facebook post. The first approach is a Naive Bayesclassifier and the second one involves lexical resources. However, an observationwas made o... |
<s>models work depends onmany parameters such as the data size, number of layers, choice of alphabets etc.Some other works have also been studied for this research. These include neuralnetwork research topics, representation with back propagating errors and otherfundamental dependency learning with the LSTM algorithm w... |
<s>a corpus.LDA mimics the way a document is written. Given the topics it generates adocument that best fits those topics and hence can understand the correlationbetween documents and topics. So this paper talks and discusses about each of thetopic modelling algorithm and critically analyses in terms of their performan... |
<s>is generated by taking a mixtureof topics and then the topics consist of word distributions. In the experimentstwo different corpora were tested to explore how the LDA can generate topicsand their corresponding words. Perplexity was calculated of the model to test theperformance in general. Perplexity is an estimate... |
<s>of LDA. Blei proposed the LDA algorithm fortopic modelling in 2003. An exploration of the underlying semantic structure of adataset from the JSTORs archive for scientific journals were carried out in [19]. Amethodology was proposed to facilitate efficient browsing of the electronic dataset.The Dynamic LDA was also i... |
<s>an enormous amount of textualdata have been and still are being generated extremely rapidly on the world wideweb (www). As soon as data science got fueled with big data, the importanceof analyzing textual data emerged. Starting from understanding the customer’sreviews across different business fields to understandin... |
<s>language. LDA is basically aprobabilistic algorithm that generates topics from a Bag of Words model. Beforethe LDA is discussed, a general overview of Topic and Term is given as follows:A topic is a collection of words with different probabilities of occurrence in adocument talking about that topic. If there are mul... |
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