DOI: 10.5281/zenodo.10157504
title: Datasets for Sentiment Analysis
title: STS-Gold Dataset
title: Amazon Sales Dataset
title: Rotten Tomatoes Reviews Dataset
title: Preprocessed dataset sentiment analysis
title: Amazon Earphones Reviews
title: Amazon Musical Instruments Reviews

description: 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.inStemmed 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)