The dataset has three sentiments namely, negative, neutral, and positive. It contains two fields for the tweet and label.

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.

SSH CENTRE (Social Sciences and Humanities for Climate, Energy aNd Transport Research Excellence) is a Horizon Europe project, engaging directly with stakeholders across research, policy, and business (including citizens) to strengthen social innovation, SSH-STEM collaboration, transdisciplinary policy advice, inclusive engagement, and SSH communities across Europe, accelerating the EU’s transition to carbon neutrality. SSH CENTRE is based in a range of activities related to Open Science, inclusivity and diversity – especially with regards Southern and Eastern Europe and different career stages – including: development of novel SSH-STEM collaborations to facilitate the delivery of the EU Green Deal; SSH knowledge brokerage to support regions in transition; and the effective design of strategies for citizen engagement in EU R&I activities. Outputs include action-led agendas and building stakeholder synergies through regular Policy Insight events.This is captured in a high-profile virtual SSH CENTRE generating and sharing best practice for SSH policy advice, overcoming fragmentation to accelerate the EU’s journey to a sustainable future.The documents uploaded here are part of WP2 whereby novel, interdisciplinary teams were provided funding to undertake activities to develop a policy recommendation related to EU Green Deal policy. Each of these policy recommendations, and the activities that inform them, will be written-up as a chapter in an edited book collection. Three books will make up this edited collection - one on climate, one on energy and one on mobility. As part of writing a chapter for the SSH CENTRE book on ‘Mobility’, we set out to analyse the sentiment of users on Twitter regarding shared and active mobility modes in Brussels. This involved us collecting tweets between 2017-2022. A tweet was collected if it contained a previously defined mobility keyword (for example: metro) and either the name of a (local) politician, a neighbourhood or municipality, or a (shared) mobility provider. The files attached to this Zenodo webpage is a csv files containing the tweets collected.”.

About Dataset

Context

This is the Twitter Sentiment Analysis dataset. It contains 1 Million tweets extracted using the Twitter Opensource API. The tweets have been annotated (0 = negative, 4 = positive) and they can be used primarily to detect sentiment.

Content It contains the following 6 fields:

target: the polarity of the tweet (0 = negative, 2 = neutral, 4 = positive)

ids: The id of the tweet ( 2087)

date: the date of the tweet (Sat April 15 23:58:44 UTC 2023)

flag: The query (lyx). If there is no query, then this value is NO_QUERY.

user: The user that tweeted (raj713335)

**text: **the text of the tweet (Lyx is cool)

Acknowledgments The official link regarding the dataset with resources about how it was generated is here The official paper detailing the approach is here

Citation: Go, A., Bhayani, R. and Huang, L., 2009. Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1(2009), p.12.

Inspiration To detect severity from tweets. You may have a look at this.

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.

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).

1) Data introduction • Twitter-tweets-sentiment dataset is a dataset that aims to analyze tweet sentiment for Twitter and natural language processing.

2) Data utilization (1)Twitter-tweets-sentiment data has characteristics that: • The data consists of three columns, including emotion and text, and aims to block negative tweets through a powerful classification model. (2) Twitter-tweets-sentiment data can be used to: • Social Media Monitoring: Businesses and organizations can use data to monitor social media platforms and gauge public sentiment about a brand, product, event, or social issue. • Sentiment analysis: This dataset can be used to train models that classify the sentiment of tweets, which can help companies and researchers understand public opinion on a variety of topics.

Context

Twitter Sentiment Analysis Dataset especially for classification using Logistic Regression

Content

• tweet - Preprocessed token array for each tweet (Preprocessing done are remove hyperlinks, remove hashtags, remove stop words and punctuation)

• bias - Just a simple bias value (default 1)

• pos - Sum of positive frequencies of each word in the tweet tokens.

• neg - Sum of negative frequencies of each word in the tweet tokens.

• label - 1.0 for Positive Tweet and 0.0 for Negative Tweet.

Acknowledgements

This dataset was part of the Week 1 Labs of Coursera Natural Language Processing Course. This dataset was custom created from scratch using NLTL Library for text preprocessing and all functions for preprocessing were from scratch.

The dataset comprises tweets labeled with sentiment ratings in an ordinal five-point scale, including classes for strongly negative, negative, neutral, positive, and strongly positive.

If you use the dataset, cite the paper: https://doi.org/10.1016/j.eswa.2022.117541

The most comprehensive dataset to date regarding climate change and human opinions via Twitter. It has the heftiest temporal coverage, spanning over 13 years, includes over 15 million tweets spatially distributed across the world, and provides the geolocation of most tweets. Seven dimensions of information are tied to each tweet, namely geolocation, user gender, climate change stance and sentiment, aggressiveness, deviations from historic temperature, and topic modeling, while accompanied by environmental disaster events information. These dimensions were produced by testing and evaluating a plethora of state-of-the-art machine learning algorithms and methods, both supervised and unsupervised, including BERT, RNN, LSTM, CNN, SVM, Naive Bayes, VADER, Textblob, Flair, and LDA.

The following columns are in the dataset:

➡ created_at: The timestamp of the tweet. ➡ id: The unique id of the tweet. ➡ lng: The longitude the tweet was written. ➡ lat: The latitude the tweet was written. ➡ topic: Categorization of the tweet in one of ten topics namely, seriousness of gas emissions, importance of human intervention, global stance, significance of pollution awareness events, weather extremes, impact of resource overconsumption, Donald Trump versus science, ideological positions on global warming, politics, and undefined. ➡ sentiment: A score on a continuous scale. This scale ranges from -1 to 1 with values closer to 1 being translated to positive sentiment, values closer to -1 representing a negative sentiment while values close to 0 depicting no sentiment or being neutral. ➡ stance: That is if the tweet supports the belief of man-made climate change (believer), if the tweet does not believe in man-made climate change (denier), and if the tweet neither supports nor refuses the belief of man-made climate change (neutral). ➡ gender: Whether the user that made the tweet is male, female, or undefined. ➡ temperature_avg: The temperature deviation in Celsius and relative to the January 1951-December 1980 average at the time and place the tweet was written. ➡ aggressiveness: That is if the tweet contains aggressive language or not.

Since Twitter forbids making public the text of the tweets, in order to retrieve it you need to do a process called hydrating. Tools such as Twarc or Hydrator can be used to hydrate tweets.

Context

Scrapped from twitters from 2016-01-01 to 2019-03-29, Collecting Tweets containing Bitcoin or BTC Tools used:

Twint Tweepy

Content

Tweet in multiple Language & Talked about Bitcoin

Acknowledgements

Thanks to Alex ( https://www.kaggle.com/alaix14 ) for his dataset (https://www.kaggle.com/alaix14/bitcoin-tweets-20160101-to-20190329 ), It is just an additional dimension where Sentiment is analyzed with a price change for Bitcoin

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.

A dataset of tweets with sentiment labels, collected from Twitter in 2009. The dataset contains 1,800,000 tweets, with each tweet having a sentiment label of positive, negative, or neutral. The dataset is a valuable resource for training and evaluating sentiment analysis models.

Utilize our Tweets dataset for a range of applications to enhance business strategies and market insights. Analyzing this dataset offers a comprehensive view of social media dynamics, empowering organizations to optimize their communication and marketing strategies. Access the full dataset or select specific data points tailored to your needs. Popular use cases include sentiment analysis to gauge public opinion and brand perception, competitor analysis by examining engagement and sentiment around rival brands, and crisis management through real-time tracking of tweet sentiment and influential voices during critical events.

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.

text -> Original tweet text, as downloaded trough the Twitter APItext_final -> Text after cleanup, as used for lexicon matchinghour -> Date and time (only hour) at which the tweet was publishedtime_cat -> Dummy. Whether the tweet was published during the event [“cuenta”] or before/after the event [“no_cuenta”]rt_count -> Number of retweets at the moment of the download of the dataurl -> Dummy. The tweet text includes a urlmedia -> Dummy. The tweet had photo or videoEmoWord -> Total number of matches for emotional words or stems in the tweet textMoralWord -> Total number of matches for moral words or stems in the tweet textEmoMoralWord -> Total number of matches for both moral and emotional words or stems in the tweet textOnlyEmoWord -> Number of matches for emotional words or stems in tweet text (excluding those which also matched moral words or stems)OnlyMoralWord -> Number of matches for moral words or stems in tweet text (excluding those which also matched emotional words or stems)foll_div10 -> Number of followers of the account that published the tweet at the moment of the data download, divided by 10,000

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.

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.

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.

A crucial part of sentiment classification is featuring extraction because it involves extracting valuable information from text data, which affects the model’s performance. The goal of this paper is to help in selecting a suitable feature extraction method to enhance the performance of sentiment analysis tasks. In order to provide directions for future machine learning and feature extraction research, it is important to analyze and summarize feature extraction techniques methodically from a machine learning standpoint. There are several methods under consideration, including Bag-of-words (BOW), Word2Vector, N-gram, Term Frequency- Inverse Document Frequency (TF-IDF), Hashing Vectorizer (HV), and Global vector for word representation (GloVe). To prove the ability of each feature extractor, we applied it to the Twitter US airlines and Amazon musical instrument reviews datasets. Finally, we trained a random forest classifier using 70% of the training data and 30% of the testing data, enabling us to evaluate and compare the performance using different metrics. Based on our results, we find that the TD-IDF technique demonstrates superior performance, with an accuracy of 99% in the Amazon reviews dataset and 96% in the Twitter US airlines dataset. This study underscores the paramount significance of feature extraction in sentiment analysis, endowing pragmatic insights to elevate model performance and steer future research pursuits.