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@@ -61,3 +61,5 @@ my_dataset/final_dataset_v4.csv filter=lfs diff=lfs merge=lfs -text
 my_dataset/small_data.csv filter=lfs diff=lfs merge=lfs -text
 my_dataset/NLP_Amazon_Dataset/final_dataset_v4.csv filter=lfs diff=lfs merge=lfs -text
 my_dataset/NLP_Amazon_Dataset/small_data.csv filter=lfs diff=lfs merge=lfs -text
+NLP_Amazon_Dataset/final_dataset_v4.csv filter=lfs diff=lfs merge=lfs -text
+NLP_Amazon_Dataset/small_data.csv filter=lfs diff=lfs merge=lfs -text

NLP_Amazon_Dataset/final_dataset_v4.csv

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NLP_Amazon_Dataset/readme.txt

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+Dataset Name: [Customer Product Review] Amazon Reviews for Computer Accessories: A Curated Dataset for Sentiment Analysis (2021-Present)
+Description:
+This dataset contains a curated collection of user reviews for computer accessories scraped from Amazon. It was specifically assembled for the development and evaluation of machine learning and deep learning models for sentiment analysis tasks. The data provides insights into contemporary customer opinions, preferences, and complaints within the technology product sector.
+The collection process utilized web scraping techniques with Beautiful Soup to gather reviews posted from 2021 onward, ensuring the data reflects recent market trends and user language. After an extensive data cleaning and deduplication process, the final dataset comprises 29,755 unique review entries.
+Each entry includes key metadata such as the review text, star rating, reviewer information, and other relevant details, making it a valuable resource for natural language processing (NLP) research.
+This dataset was used in a comparative study that implemented and evaluated a range of sentiment classification models, including:
+•	Deep Learning Models: Convolutional Neural Networks (CNNs) and Bidirectional Encoder Representations from Transformers (BERT).
+•	Traditional Machine Learning Models: Support Vector Machines (SVM) and Logistic Regression.
+•	Explainable AI (XAI): The study also employed LIME (Local Interpretable Model-agnostic Explanations) to interpret model predictions and enhance transparency.
+The results demonstrated the superior performance of the BERT model, achieving an accuracy of 86.01% and a balanced F1-score of 88%, establishing it as a robust choice for understanding sentiment in customer feedback.
+Potential Research Applications:
+•	Sentiment Analysis and Opinion Mining
+•	Comparative Analysis of NLP Models (Traditional ML vs. Deep Learning)
+•	Explainable AI (XAI) in NLP
+•	Customer Behavior and Market Trend Analysis
+•	Benchmarking for New Classification Algorithms
+
+Author: MD Abdullah Al Symum; Rafiatul Zannah; Subarna Yeasmin Sheemu;
+Affiliation: Department of Computer Science and Engineering, 
+Brac University, Dhaka, Bangladesh;
+Correspondence: symumsani08@gmail.com; rafiazannah15@gmail.com;
+subarna.yeasmin.sheemu@g.bracu.ac.bd; 
+
+Dataset DOI: 10.5281/zenodo.17053541
+Paper DOI: https://doi.org/10.1109/ICCIT60459.2023.10441344
+
+
+
+Dataset Details
+Total Samples: 29,755
+Language: [e.g., Bengali, English, or Multilingual]
+Task: Sentiment Analysis / Text Classification
+License: Creative Commons Attribution 4.0 International (CC BY 4.0)
+Release Date: [January 2024]
+
+Data Collection
+The dataset was compiled by scraping publicly available customer reviews for computer accessories from the Amazon.com marketplace. The collection period spanned from January 2021 to June 2023, ensuring the data reflects recent consumer trends. The scraping process was performed using Python's Beautiful Soup library, targeting only the publicly visible review sections and adhering to the website's robots.txt file.
+All personally identifiable information (PII) was meticulously excluded during collection. No user names, profile links, or other identifiers were stored. The dataset focuses solely on the review content (text and star rating) and related product metadata for the purpose of sentiment analysis. This approach ensures compliance with ethical research guidelines for using publicly available data.
+
+Usage
+This dataset is intended for academic research in NLP, particularly for:
+•	Sentiment Analysis
+•	Text Classification model benchmarking
+•	Comparative studies of language models (e.g., LSTM vs. Transformer-based models)
+•	Low-resource language NLP (if applicable)
+
+Citation
+If you use this dataset in your research, please cite both our conference paper and the dataset itself.
+
+Paper Link: https://ieeexplore.ieee.org/document/11022600
+
+Cite the Paper:
+@INPROCEEDINGS{11022600,
+  author={Sheemu, Subarna Yeasmin and Symum, MD Abdullah Al and Zaman, Arsi and Asif, Abu Saleh Md. and Shakil, Arif and Zannah, Rafiatul},
+  booktitle={2024 27th International Conference on Computer and Information Technology (ICCIT)}, 
+  title={Sentimental Analysis of Customer Product Reviews to Understand Customer Needs Using Machine Learning}, 
+  year={2024},
+  volume={},
+  number={},
+  pages={1164-1169},
+  keywords={Support vector machines;Measurement;Sentiment analysis;Logistic regression;Reviews;Redundancy;Focusing;Information age;Feature extraction;Product development;Machine learning;Natural language processing;Sentimental analysis;BERT;CNN},
+  doi={10.1109/ICCIT64611.2024.11022600}
+}
+
+Acknowledgements
+We thank the International Conference on Computer and Information Technology (ICCIT) for providing a platform to present our initial findings. We also acknowledge the contributors who assisted in data collection and annotation.

NLP_Amazon_Dataset/small_data.csv

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