This dataset contains tweets labeled for sentiment analysis, categorized into Positive, Negative, and Neutral sentiments. The dataset includes tweet IDs, user metadata, sentiment labels, and tweet text, making it suitable for Natural Language Processing (NLP), machine learning, and AI-based sentiment classification research. Originally sourced from Kaggle, this dataset is curated for improved usability in social media sentiment analysis.

Dataset Description

The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their sentiment.

The dataset holds 11,932 documents annotated with 3 labels:

sentiments = { "LABEL_0": "Bearish", "LABEL_1": "Bullish", "LABEL_2": "Neutral" }

The data was collected using the Twitter API. The current dataset supports the multi-class classification… See the full description on the dataset page: https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment.

Context

The dataset has three sentiments namely, negative(-1), neutral(0), and positive(+1). It contains two fields for the tweet and label.

Acknowledgements

HUSSEIN, SHERIF (2021), “Twitter Sentiments Dataset”, Mendeley Data, V1, doi: 10.17632/z9zw7nt5h2.1

Our Twitter Sentiment Analysis Dataset provides a comprehensive collection of tweets, enabling businesses, researchers, and analysts to assess public sentiment, track trends, and monitor brand perception in real time. This dataset includes detailed metadata for each tweet, allowing for in-depth analysis of user engagement, sentiment trends, and social media impact.

Key Features:

  Tweet Content & Metadata: Includes tweet text, hashtags, mentions, media attachments, and engagement metrics such as likes, retweets, and replies.
  Sentiment Classification: Analyze sentiment polarity (positive, negative, neutral) to gauge public opinion on brands, events, and trending topics.
  Author & User Insights: Access user details such as username, profile information, follower count, and account verification status.
  Hashtag & Topic Tracking: Identify trending hashtags and keywords to monitor conversations and sentiment shifts over time.
  Engagement Metrics: Measure tweet performance based on likes, shares, and comments to evaluate audience interaction.
  Historical & Real-Time Data: Choose from historical datasets for trend analysis or real-time data for up-to-date sentiment tracking.


Use Cases:

  Brand Monitoring & Reputation Management: Track public sentiment around brands, products, and services to manage reputation and customer perception.
  Market Research & Consumer Insights: Analyze consumer opinions on industry trends, competitor performance, and emerging market opportunities.
  Political & Social Sentiment Analysis: Evaluate public opinion on political events, social movements, and global issues.
  AI & Machine Learning Applications: Train sentiment analysis models for natural language processing (NLP) and predictive analytics.
  Advertising & Campaign Performance: Measure the effectiveness of marketing campaigns by analyzing audience engagement and sentiment.



  Our dataset is available in multiple formats (JSON, CSV, Excel) and can be delivered via API, cloud storage (AWS, Google Cloud, Azure), or direct download. 
  Gain valuable insights into social media sentiment and enhance your decision-making with high-quality, structured Twitter data.

This dataset contains over 26 million English-language tweets related to Bitcoin (BTC), collected between 2013 and 2023. The data was sourced from Kaggle and includes posts from a wide range of users, from everyday investors to high-profile figures. Each tweet includes metadata such as timestamp, user information, and text content. The dataset has been thoroughly cleaned to remove spam, non-English content, bot activity, and duplicated entries. It serves as the primary input for sentiment analysis and subsequent price prediction models in this study.

TweetSentimentClassification An MTEB dataset Massive Text Embedding Benchmark

A multilingual Sentiment Analysis dataset consisting of tweets in 8 different languages.

Task category t2c

Domains Social, Written

Reference https://aclanthology.org/2022.lrec-1.27

  How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code: import mteb

task = mteb.get_tasks(["TweetSentimentClassification"]) evaluator =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/tweet_sentiment_multilingual.

Context

The following information can also be found at https://www.kaggle.com/davidwallach/financial-tweets. Out of curosity, I just cleaned the .csv files to perform a sentiment analysis. So both the .csv files in this dataset are created by me.

Anything you read in the description is written by David Wallach and using all this information, I happen to perform my first ever sentiment analysis.

"I have been interested in using public sentiment and journalism to gather sentiment profiles on publicly traded companies. I first developed a Python package (https://github.com/dwallach1/Stocker) that scrapes the web for articles written about companies, and then noticed the abundance of overlap with Twitter. I then developed a NodeJS project that I have been running on my RaspberryPi to monitor Twitter for all tweets coming from those mentioned in the content section. If one of them tweeted about a company in the stocks_cleaned.csv file, then it would write the tweet to the database. Currently, the file is only from earlier today, but after about a month or two, I plan to update the tweets.csv file (hopefully closer to 50,000 entries.

I am not quite sure how this dataset will be relevant, but I hope to use these tweets and try to generate some sense of public sentiment score."

Content

This dataset has all the publicly traded companies (tickers and company names) that were used as input to fill the tweets.csv. The influencers whose tweets were monitored were: ['MarketWatch', 'business', 'YahooFinance', 'TechCrunch', 'WSJ', 'Forbes', 'FT', 'TheEconomist', 'nytimes', 'Reuters', 'GerberKawasaki', 'jimcramer', 'TheStreet', 'TheStalwart', 'TruthGundlach', 'Carl_C_Icahn', 'ReformedBroker', 'benbernanke', 'bespokeinvest', 'BespokeCrypto', 'stlouisfed', 'federalreserve', 'GoldmanSachs', 'ianbremmer', 'MorganStanley', 'AswathDamodaran', 'mcuban', 'muddywatersre', 'StockTwits', 'SeanaNSmith'

Acknowledgements

The data used here is gathered from a project I developed : https://github.com/dwallach1/StockerBot

Inspiration

I hope to develop a financial sentiment text classifier that would be able to track Twitter's (and the entire public's) feelings about any publicly traded company (and cryptocurrency)

This dataset is a processed version of the Sentiment140 corpus, containing 1.6 million tweets with binary sentiment labels. The original data has been cleaned, tokenized, and prepared for natural language processing (NLP) and machine learning tasks. It provides a rich resource for sentiment analysis, text classification, and other NLP applications. The dataset includes the full processed corpus (train-processed.csv) and a smaller sample of 10,000 tweets (train-processed-sample.csv) for quick experimentation and model prototyping. Key Features:

1.6 million labeled tweets Binary sentiment classification (0 for negative, 1 for positive) Preprocessed and tokenized text Balanced class distribution Suitable for various NLP tasks and model architectures

Citation If you use this dataset in your research or project, please cite the original Sentiment140 dataset: Go, A., Bhayani, R. and Huang, L., 2009. Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1(2009), p.12.

Dataset Card for cardiffnlp/tweet_sentiment_multilingual

  Dataset Summary

Tweet Sentiment Multilingual consists of sentiment analysis dataset on Twitter in 8 different lagnuages.

arabic english french german hindi italian portuguese spanish

  Supported Tasks and Leaderboards

text_classification: The dataset can be trained using a SentenceClassification model from HuggingFace transformers.

  Dataset Structure





  Data Instances

An instance from… See the full description on the dataset page: https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual.

Dataset contains airline-related tweets that were labeled with positive, negative, and neutral sentiment.

Context and Methodology:

This dataset was created as part of a sentiment analysis project using enriched Twitter data. The objective was to train and test a machine learning model to automatically classify the sentiment of tweets (e.g., Positive, Negative, Neutral).
The data was generated using tweets that were sentiment-scored with a custom sentiment scorer. A machine learning pipeline was applied, including text preprocessing, feature extraction with CountVectorizer, and prediction with a HistGradientBoostingClassifier.

Technical Details:

The dataset includes five main files:

• test_predictions_full.csv – Predicted sentiment labels for the test set.

• sentiment_model.joblib – Trained machine learning model.

• count_vectorizer.joblib – Text feature extraction model (CountVectorizer).

• model_performance.txt – Evaluation metrics and performance report of the trained model.

• confusion_matrix.png – Visualization of the model’s confusion matrix.

The files follow standard naming conventions based on their purpose.
The .joblib files can be loaded into Python using the joblib and scikit-learn libraries.
The .csv,.txt, and .png files can be opened with any standard text reader, spreadsheet software, or image viewer.
Additional performance documentation is included within the model_performance.txt file.

Additional Details:

• The data was constructed to ensure reproducibility.

• No personal or sensitive information is present.

• It can be reused by researchers, data scientists, and students interested in Natural Language Processing (NLP), machine learning classification, and sentiment analysis tasks.

Wider spatiotemporal English COVID-19 Tweets

Context

This is the Sentiment dataset. The tweets have been annotated with 4 different categories(positive,negative,uncertainty,litigious) and they can be used to detect sentiment .

Content

It contains the following 3 fields: - Language - Text - Label

EleutherAI/twitter-sentiment dataset hosted on Hugging Face and contributed by the HF Datasets community

Sentiment140 consists of Twitter messages with emoticons, which are used as noisy labels for sentiment classification. For more detailed information please refer to the paper.

Dataset

This dataset was created by Sayan Golder

Released under MIT

Contents

This dataset is collected and annotated for the SMILE project http://www.culturesmile.org. This collection of tweets mentioning 13 Twitter handles associated with British museums was gathered between May 2013 and June 2015. It was created for the purpose of classifying emotions, expressed on Twitter towards arts and cultural experiences in museums. It contains 3,085 tweets, with 5 emotions namely anger, disgust, happiness, surprise and sadness. Please see our paper "SMILE: Twitter Emotion Classification using Domain Adaptation" for more details of the dataset.License: The annotations are provided under a CC-BY license, while Twitter retains the ownership and rights of the content of the tweets.

AfriSenti is the largest sentiment analysis benchmark dataset for under-represented African languages---covering 110,000+ annotated tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and yoruba).

Rusya-Ukrayna Savaşı Twitter Duygu Analizi Veri Seti

Bu veri seti, TÜBİTAK projemiz kapsamında Rusya-Ukrayna savaşıyla ilgili Twitter/X paylaşımlarının duygu analizi için hazırlanmıştır. Veriler açık kaynak veri setlerinden toplanmış, filtrelenmiş ve model eğitimi için düzenlenmiştir. Paylaşılan dosyada kullanıcı adı, kullanıcı id'si, profil bağlantısı gibi alanlar bulunmamaktadır.

  Dosyalar

train.csv: Temizlenmiş metin ve duygu etiketi içeren ana veri dosyası.… See the full description on the dataset page: https://huggingface.co/datasets/Batuhanbey/rusya-ukrayna-twitter-sentiment-dataset.

General Description

This dataset comprises 4,038 tweets in Spanish, related to discussions about artificial intelligence (AI), and was created and utilized in the publication "Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights," (10.1109/IE61493.2024.10599899) presented at the 20th International Conference on Intelligent Environments. It is designed to support research on public perception, sentiment, and engagement with AI topics on social media from a Spanish-speaking perspective. Each entry includes detailed annotations covering sentiment analysis, user engagement metrics, and user profile characteristics, among others.

Data Collection Method

Tweets were gathered through the Twitter API v1.1 by targeting keywords and hashtags associated with artificial intelligence, focusing specifically on content in Spanish. The dataset captures a wide array of discussions, offering a holistic view of the Spanish-speaking public's sentiment towards AI.

Dataset Content

ID: A unique identifier for each tweet.

text: The textual content of the tweet. It is a string with a maximum allowed length of 280 characters.

polarity: The tweet's sentiment polarity (e.g., Positive, Negative, Neutral).

favorite_count: Indicates how many times the tweet has been liked by Twitter users. It is a non-negative integer.

retweet_count: The number of times this tweet has been retweeted. It is a non-negative integer.

user_verified: When true, indicates that the user has a verified account, which helps the public recognize the authenticity of accounts of public interest. It is a boolean data type with two allowed values: True or False.

user_default_profile: When true, indicates that the user has not altered the theme or background of their user profile. It is a boolean data type with two allowed values: True or False.

user_has_extended_profile: When true, indicates that the user has an extended profile. An extended profile on Twitter allows users to provide more detailed information about themselves, such as an extended biography, a header image, details about their location, website, and other additional data. It is a boolean data type with two allowed values: True or False.

user_followers_count: The current number of followers the account has. It is a non-negative integer.

user_friends_count: The number of users that the account is following. It is a non-negative integer.

user_favourites_count: The number of tweets this user has liked since the account was created. It is a non-negative integer.

user_statuses_count: The number of tweets (including retweets) posted by the user. It is a non-negative integer.

user_protected: When true, indicates that this user has chosen to protect their tweets, meaning their tweets are not publicly visible without their permission. It is a boolean data type with two allowed values: True or False.

user_is_translator: When true, indicates that the user posting the tweet is a verified translator on Twitter. This means they have been recognized and validated by the platform as translators of content in different languages. It is a boolean data type with two allowed values: True or False.

Cite as

Guerrero-Contreras, G., Balderas-Díaz, S., Serrano-Fernández, A., & Muñoz, A. (2024, June). Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights. In 2024 International Conference on Intelligent Environments (IE) (pp. 62-69). IEEE.

Potential Use Cases

This dataset is aimed at academic researchers and practitioners with interests in:

Sentiment analysis and natural language processing (NLP) with a focus on AI discussions in the Spanish language.

Social media analysis on public engagement and perception of artificial intelligence among Spanish speakers.

Exploring correlations between user engagement metrics and sentiment in discussions about AI.

Data Format and File Type

The dataset is provided in CSV format, ensuring compatibility with a wide range of data analysis tools and programming environments.

License

The dataset is available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, permitting sharing, copying, distribution, transmission, and adaptation of the work for any purpose, including commercial, provided proper attribution is given.