A Reddit (r/teaching community) dataset of 1,304,575 comments from r/teaching with 5-class sentiment scores, pre/post COVID labels (438,027 pre, 866,548 post), and relational language detection, collected by Cai et al. via Reddit API (2024). Each record contains comment text, post title, post selftext, created_utc, score, reply_count, pandemic_period (0=pre/1=post COVID), predicted_rating (1-5), normalized_sentiment (-1 to +1), contains_relational_word, sentiment_probabilities. 1,304,575 r/teaching comments with fine-grained 5-class sentiment analysis spanning pre/post COVID — enabling longitudinal study of how teaching community discourse and emotional tone shifted during the pandemic. Suitable for community sentiment analysis, COVID-19 discourse shifts, longitudinal NLP, education community research, relational language detection, sentiment classification.
A Twitter dataset of 1,600,000 tweets with binary sentiment labels (positive/negative) assigned via emoticon distant supervision, collected by Go, Bhayani and Huang at Stanford University via Twitter streaming API in 2009. Each record contains tweet text with polarity label (0=negative, 4=positive), tweet ID, timestamp, search query used for collection, and username. The dataset is notable for its one of the largest and most widely used Twitter sentiment benchmarks, notable for using emoticons as a noisy but scalable distant supervision signal rather than manual annotation, enabling 1.6M labeled examples. Intended for research into sentiment analysis, opinion mining, distant supervision research, text classification benchmarking, and social media language modeling.
Synthetic data: 150,000 generated social-media posts about Nigerian telecom operators (MTN, Airtel, Glo, 9mobile), published on Hugging Face by THUgewu as 'africa-synth'. The posts are not real user content. The file groups them into 978 rows by platform, operator, city and sentiment, with one list per field (post ID, time, text, issue category, likes, shares, comments, reach, sentiment score, operator response). Suitable for testing sentiment and customer-service pipelines, not for studying real users.
A Twitter (Nigerian language communities) dataset of 60,823 tweet IDs across 4 Nigerian languages with sentiment labels, collected by Muhammad et al. via Twitter API (2022). Each record contains tweet ID, sentiment label (positive/negative/neutral), language, split (train/dev/test). First large-scale sentiment corpus for Nigerian languages — 60,823 annotated tweet IDs across Hausa, Igbo, Yoruba, and Nigerian Pidgin, enabling low-resource African NLP research.. Suitable for low-resource NLP, African language sentiment analysis, multilingual classification, cross-lingual transfer..
A Reddit dataset of 54,075 subreddits and 571,927 directed hyperlinks (510,787 positive sentiment, 61,140 negative sentiment) from post hyperlinks embedded in post titles, collected by Kumar et al. via Reddit API, January 2014 to April 2017, distributed by SNAP. Each record contains directed edges from source to target subreddit with binary sentiment label (+1/-1) and 86-dimension text property vector; post ID and timestamp per edge. captures cross-community interaction and conflict on Reddit with sentiment polarity per hyperlink, enabling study of how communities reference each other positively vs. antagonistically over time. Suitable for community conflict detection, cross-community interaction modeling, signed network analysis, temporal network analysis, and online community dynamics research.
A Reddit dataset of 35,776 subreddits and 286,561 directed hyperlinks (265,491 positive sentiment, 21,070 negative sentiment) from post hyperlinks embedded in post bodies, collected by Kumar et al. via Reddit API, January 2014 to April 2017, distributed by SNAP. Each record contains directed edges from source to target subreddit with binary sentiment label (+1/-1) and 86-dimension text property vector; post ID and timestamp per edge. captures cross-community interaction and conflict on Reddit with sentiment polarity per hyperlink, enabling study of how communities reference each other positively vs. antagonistically over time. Suitable for community conflict detection, cross-community interaction modeling, signed network analysis, temporal network analysis, and online community dynamics research.
A Twitter dataset of 11,929 labeled financial tweets, collected by zeroshot via Twitter API. Each record contains tweet text, sentiment label (positive/negative/neutral), sentiment value (0/1/2), train/valid split. A labeled dataset of tweets about financial news, with sentiment annotations for stock market-related content. Suitable for sentiment analysis, financial NLP, stock market prediction, social media analytics, natural language processing.
A Twitter dataset of 5,171 sentiment-labeled tweets about NFL teams, collected by Cameron C (Kaggle user cammyc) via Twitter API. Each record contains tweet text, sentiment (positive/negative/neutral), team, username, timestamp, confidence score. A curated dataset of 3,701 NFL-related tweets with sentiment labels, enabling research on sports fan sentiment, team-specific public opinion, and social media engagement patterns during the NFL season. Suitable for sentiment analysis, sports marketing, fan engagement, public opinion tracking, temporal sentiment analysis.
Reddit
Twitter (X)
Facebook
Instagram