Datasets for Sentiment Analysis

This repository was created for my Master's thesis in Computational Intelligence and Internet of Things at the University of Córdoba, Spain. The purpose of this repository is to store the datasets found that were used in some of the studies that served as research material for this Master's thesis. Also, the datasets used in the experimental part of this work are included. Below are the datasets specified, along with the details of their references, authors, and download sources. ----------- STS-Gold Dataset ---------------- The dataset consists of 2026 tweets. The file consists of 3 columns: id, polarity, and tweet. The three columns denote the unique id, polarity index of the text and the tweet text respectively. Reference: Saif, H., Fernandez, M., He, Y., & Alani, H. (2013). Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold. File name: sts_gold_tweet.csv ----------- Amazon Sales Dataset ---------------- This dataset is having the data of 1K+ Amazon Product's Ratings and Reviews as per their details listed on the official website of Amazon. The data was scraped in the month of January 2023 from the Official Website of Amazon. Owner: Karkavelraja J., Postgraduate student at Puducherry Technological University (Puducherry, Puducherry, India) Features: product_id - Product ID product_name - Name of the Product category - Category of the Product discounted_price - Discounted Price of the Product actual_price - Actual Price of the Product discount_percentage - Percentage of Discount for the Product rating - Rating of the Product rating_count - Number of people who voted for the Amazon rating about_product - Description about the Product user_id - ID of the user who wrote review for the Product user_name - Name of the user who wrote review for the Product review_id - ID of the user review review_title - Short review review_content - Long review img_link - Image Link of the Product product_link - Official Website Link of the Product License: CC BY-NC-SA 4.0 File name: amazon.csv ----------- Rotten Tomatoes Reviews Dataset ---------------- This rating inference dataset is a sentiment classification dataset, containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. On average, these reviews consist of 21 words. The first 5331 rows contains only negative samples and the last 5331 rows contain only positive samples, thus the data should be shuffled before usage. This data is collected from https://www.cs.cornell.edu/people/pabo/movie-review-data/ as a txt file and converted into a csv file. The file consists of 2 columns: reviews and labels (1 for fresh (good) and 0 for rotten (bad)). Reference: Bo Pang and Lillian Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pages 115–124, Ann Arbor, Michigan, June 2005. Association for Computational Linguistics File name: data_rt.csv ----------- Preprocessed Dataset Sentiment Analysis ---------------- Preprocessed amazon product review data of Gen3EcoDot (Alexa) scrapped entirely from amazon.in Stemmed and lemmatized using nltk. Sentiment labels are generated using TextBlob polarity scores. The file consists of 4 columns: index, review (stemmed and lemmatized review using nltk), polarity (score) and division (categorical label generated using polarity score). DOI: 10.34740/kaggle/dsv/3877817 Citation: @misc{pradeesh arumadi_2022, title={Preprocessed Dataset Sentiment Analysis}, url={https://www.kaggle.com/dsv/3877817}, DOI={10.34740/KAGGLE/DSV/3877817}, publisher={Kaggle}, author={Pradeesh Arumadi}, year={2022} } This dataset was used in the experimental phase of my research. File name: EcoPreprocessed.csv ----------- Amazon Earphones Reviews ---------------- This dataset consists of a 9930 Amazon reviews, star ratings, for 10 latest (as of mid-2019) bluetooth earphone devices for learning how to train Machine for sentiment analysis. This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset. The file consists of 5 columns: ReviewTitle, ReviewBody, ReviewStar, Product and division (manually added - categorical label generated using ReviewStar score) License: U.S. Government Works Source: www.amazon.in File name (original): AllProductReviews.csv (contains 14337 reviews) File name (edited - used for my research) : AllProductReviews2.csv (contains 9930 reviews) ----------- Amazon Musical Instruments Reviews ---------------- This dataset contains 7137 comments/reviews of different musical instruments coming from Amazon. This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset. The file consists of 10 columns: reviewerID, asin (ID of the product), reviewerName, helpful (helpfulness rating of the review), reviewText, overall (rating of the product), summary (summary of the review), unixReviewTime (time of the review - unix time), reviewTime (time of the review (raw) and division (manually added - categorical label generated using overall score). Source: http://jmcauley.ucsd.edu/data/amazon/ File name (original): Musical_instruments_reviews.csv (contains 10261 reviews) File name (edited - used for my research) : Musical_instruments_reviews2.csv (contains 7137 reviews)

本仓库为本人在西班牙科尔多瓦大学（University of Córdoba）攻读计算智能与物联网方向硕士学位的毕业论文所创建，用于存储本人硕士论文研究素材中涉及的部分研究所使用的数据集，同时包含本研究实验环节所用到的数据集。下文将列出指定数据集及其参考文献、作者与下载来源详情。
----------- STS-Gold数据集（STS-Gold Dataset） -----------
该数据集包含2026条推文（tweet），文件包含三列：id、极性（polarity）与推文内容。三列分别代表唯一标识符、文本极性索引以及推文文本。
参考文献：Saif, H., Fernandez, M., He, Y., & Alani, H. (2013). 用于推特情感分析的评估数据集：综述与全新数据集STS-Gold。
文件名：sts_gold_tweet.csv
----------- 亚马逊销售数据集（Amazon Sales Dataset） -----------
该数据集包含1000余条亚马逊产品评分与评论数据，数据源自亚马逊官方网站公开的产品详情信息。数据于2023年1月从亚马逊官网爬取获得。
数据所有者：Karkavelraja J.，印度本地治里理工大学（Puducherry Technological University）研究生
数据集特征字段如下：
- product_id：产品唯一标识符
- product_name：产品名称
- category：产品所属类别
- discounted_price：产品折扣后价格
- actual_price：产品原价
- discount_percentage：产品折扣率
- rating：产品评分
- rating_count：参与该产品评分的用户总数
- about_product：产品详情描述
- user_id：撰写该评论的用户唯一标识符
- user_name：撰写该评论的用户名
- review_id：评论唯一标识符
- review_title：评论短标题
- review_content：评论详细内容
- img_link：产品图片链接
- product_link：产品官方网站链接
许可协议：CC BY-NC-SA 4.0
文件名：amazon.csv
----------- 烂番茄评论数据集（Rotten Tomatoes Reviews Dataset） -----------
该数据集为情感分类任务的评分推理数据集，包含来自烂番茄电影评论的5331条正向处理语句与5331条负向处理语句，单条评论平均包含21个单词。数据集前5331行为负样本，后5331行为正样本，因此使用前需对数据进行洗牌（shuffle）操作。
该数据源自https://www.cs.cornell.edu/people/pabo/movie-review-data/，原格式为txt文件，后转换为CSV格式。文件包含两列：评论内容（reviews）与标签（labels，1代表好评，0代表差评）。
参考文献：Bo Pang与Lillian Lee. 星级分类：针对评分尺度的情感分类中利用类别关系. 见第43届国际计算语言学协会年会（ACL'05）论文集，美国密歇根州安阿伯，2005年6月，第115-124页。国际计算语言学协会（Association for Computational Linguistics）
文件名：data_rt.csv
----------- 情感分析预处理数据集（Preprocessed Dataset Sentiment Analysis） -----------
该数据集为从亚马逊印度站（amazon.in）爬取的Gen3EcoDot（亚马逊智能音箱Alexa相关）产品评论预处理数据，使用自然语言工具包（Natural Language Toolkit, NLTK）进行词干提取与词形还原处理，通过TextBlob极性评分生成情感标签。
文件包含四列：索引（index）、评论内容（经NLTK处理的词干化与词形还原后的评论）、极性（评分）与类别（基于极性评分生成的分类标签）。
DOI：10.34740/kaggle/dsv/3877817
引用信息：@misc{pradeesh arumadi_2022, title={Preprocessed Dataset Sentiment Analysis}, url={https://www.kaggle.com/dsv/3877817}, DOI={10.34740/KAGGLE/DSV/3877817}, publisher={Kaggle}, author={Pradeesh Arumadi}, year={2022} }
该数据集用于本人研究的实验阶段。
文件名：EcoPreprocessed.csv
----------- 亚马逊耳机评论数据集（Amazon Earphones Reviews） -----------
该数据集包含9930条亚马逊评论与星级评分，对应2019年中期推出的10款最新蓝牙音频设备，用于学习如何训练机器学习模型进行情感分析。本数据集用于本人研究的实验阶段。为契合本研究目标，本人从原始数据集中剔除了部分评论，并新增了一列数据。
文件包含五列：ReviewTitle（评论标题）、ReviewBody（评论正文）、ReviewStar（评论星级）、Product（产品名称）与division（手动新增列，基于ReviewStar评分生成的分类标签）。
许可协议：美国政府作品（U.S. Government Works）
数据来源：www.amazon.in
原始文件名：AllProductReviews.csv（包含14337条评论）
研究使用文件名：AllProductReviews2.csv（包含9930条评论）
----------- 亚马逊乐器评论数据集（Amazon Musical Instruments Reviews） -----------
该数据集包含来自亚马逊的7137条不同乐器的评论内容，用于本人研究的实验阶段。为契合本研究目标，本人从原始数据集中剔除了部分评论，并新增了一列数据。
文件包含10列：reviewerID（评论者ID）、asin（产品ID）、reviewerName（评论者姓名）、helpful（评论有用性评分）、reviewText（评论正文）、overall（产品总评分）、summary（评论摘要）、unixReviewTime（评论时间，Unix时间戳格式）、reviewTime（原始格式评论时间）与division（手动新增列，基于overall评分生成的分类标签）。
数据来源：http://jmcauley.ucsd.edu/data/amazon/
原始文件名：Musical_instruments_reviews.csv（包含10261条评论）
研究使用文件名：Musical_instruments_reviews2.csv（包含7137条评论）

搜集汇总

背景与挑战

背景概述

该数据集是一个情感分析数据集集合，包含六个子数据集，来源包括推特、亚马逊产品评论和烂番茄电影评论，覆盖文本、评分和元数据，用于情感分析研究。这些数据集经过整理或预处理，适用于机器学习实验和学术分析，具有多样化的应用场景和标注信息。