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

Overview

This dataset contains tweets related to major US airlines and is widely used for NLP and sentiment analysis tasks. Each record includes the tweet text, timestamp, airline name, and sentiment label (positive, negative, neutral). This uploaded version is prepared to support advanced text processing, machine learning, and anomaly detection experiments.

What's Included

• Tweets.csv – Full collection of airline-related tweets
• Text content suitable for NLP tasks
• Timestamp information (useful for time-based analysis)
• Sentiment labels for classification and evaluation
• Cleaned text field for direct use in ML pipelines

Purpose of This Dataset

This dataset is used in a machine learning workflow focused on: - sentiment analysis
- embedding generation (transformers)
- dimensionality reduction (PCA, UMAP)
- clustering and visualization
- unsupervised anomaly detection using Isolation Forest

It is especially suited for exploring changes in public sentiment, event detection, and contextual analysis in social media data.

Key Use Cases

• Building and testing NLP models
• Semantic similarity and embedding-based analysis
• Sentiment classification
• Detecting anomalous posts or time periods
• Visualizing tweet clusters using UMAP
• Studying customer feedback patterns in the airline industry

Source

Originally derived from the Twitter US Airline Sentiment dataset on Kaggle.
This uploaded version is intended for educational, analytical, and research purposes.

Notes

If you're using this dataset in a notebook, ensure you update your file path accordingly: ```python df = pd.read_csv("/kaggle/input/twitter-airline-sentiment-dataset/Tweets.csv")

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.

Twitter tweet data can be used for sentiment analysis for Bitcoin.

1. Preprocessing on the tweet text has already been done very rudimentarily, you can omit it.
2. The sentiment polarity score should be removed, it too acts as a classifier.
3. The final column is the classifier.
4. If you can use more than just the text data, that will add multi-modality to your functionality.
5. Enough data points are provided.

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.

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 contains over 690,000 tweets labeled as Positive, Negative, or Neutral. The data can be used for sentiment analysis and natural language processing tasks. The tweets span various topics, making this a versatile dataset for training and evaluating machine learning models. The dataset was collected and labeled through. It offers a balanced distribution of sentiments to enable robust analysis

Sentiment Distribution: Positive: 248,516 (35.9%) Negative: 244,146 (35.3%) Neutral: 198,586 (28.7%)

Dataset Card for Twitter US Airline Sentiment

  Dataset Summary

This data originally came from Crowdflower's Data for Everyone library. As the original source says,

A sentiment analysis job about the problems of each major U.S. airline. Twitter data was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as "late flight" or "rude service").

The data… See the full description on the dataset page: https://huggingface.co/datasets/osanseviero/twitter-airline-sentiment.

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

This dataset contains 1,587,808 Halal tourism related tweets from 2006 to 2021. User identification information are removed to maintain data privacy. The data was collected using the Twitter Academic API.

The data is stored in as a RDS file (R Programing Language file), where each line represents a tweet. The RDS file provides information on the following fields: lang: language of the tweet text: the full content of the tweet created_at: the publication date of the tweet location: the country of the author of the tweet

This dataset is created by merging and cleaning three sentiment analysis datasets:
- sentiment140 (kazanova/sentiment140)
- twitter-tweets-sentiment-dataset (yasserh/twitter-tweets-sentiment-dataset)
- twitter-sentiment-dataset (saurabhshahane/twitter-sentiment-dataset)

The resulting dataset consists of 1,628,166 entries with the following columns:

• target: Sentiment label of the text (numeric representation: 0 for negative, 4 for positive).
• text: Original text from the corpus.
• text_clean: The text after cleaning (removal of noise, special characters, etc.).
• text_length: Length of the original text (number of words).
• text_clean_length: Length of the cleaned text (number of words).

This dataset is ideal for sentiment analysis and text classification tasks.

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

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.

This dataset is aimed to the task of sentiment analysis in tweets about the COVID-19 pandemic. There are 3 versions of the dataset, composed by 186,000, 132,000, and 82,000 tweets in English language with stopwords removal, respectively. Positive tweets have polarity equal to 1, while negative tweets have polarity equal to 0 in all versions. All datasets were selected, cleaned and organized from the public dataset available at https://ieee-dataport.org/open-access/coronavirus-covid-19-tweets-dataset. The datasets are accompanied by embedding matrices generated from the pre-trained Word2Vec shallow neural network available at https://data.mendeley.com/datasets/t8bxg423yk/1.

This dataset is made up of unique annotated English-Malay code-switching, pure English, and pure Malay tweets using raw_tweets_012019_to_062020.csv on Kaggle (Carlson, 2020). The raw tweets file is the collected users’ tweets about a Malaysian brand called, ‘The dUCk Group’ which is founded by Vivy Yusof focuses on selling scarves, bags, cosmetics, stationaries, and Home & Living products. When preparing this dataset, the duplicated, invalid and unusable data rows are removed. The tweets are then annotated with the language category “ENG” for pure English tweets, “BM” for pure Malay tweets, and “ENG-BM” for the code-switching tweets. Besides, the tweets are annotated with sentiment value 0 for neutral, 1 for positive, and -1 for negative.

The sub-folders contain in this dataset are as follows:

1) Full Training Dataset: This sub-folder contains a full set of annotated pure English, pure Malay, and English-Malay code-switching tweets regarding ‘The dUCk Group’ brand, which can be used to train machine learning models. The tweets are kept in both CSV and XML format files namely 'full_training_dataset.csv' and 'full_training_dataset.xml'.

2) Full Testing Dataset: This sub-folder contains a full set of annotated pure English, pure Malay, and English-Malay code-switching tweets regarding ‘The dUCk Group’ brand, which can be used to test the performance of learning models. The tweets are kept in both CSV and XML format files namely 'full_testing_dataset.csv' and 'full_testing_dataset.xml'.

3) Code-Switching Training Dataset: This sub-folder comprises only annotated English-Malay code-switching tweets regarding ‘The dUCk Group’ brand for training the learning models. The tweets are kept in both CSV and XML format files namely 'eng_malay_training_dataset.csv' and 'eng_malay_training_dataset.xml'.

4) Code-Switching Testing Dataset: This sub-folder comprises only annotated English-Malay code-switching tweets regarding ‘The dUCk Group’ brand, which can be used to evaluate the performance of the learning models. The tweets are kept in both CSV and XML format files namely 'eng_malay_testing_dataset.csv' and 'eng_malay_testing_dataset.xml.

*Note: 'Language' column represents the language category of the tweet belongs to 'TweetText' column represents the whole tweet 'TweetSentiment' column represents the sentiment value of the tweet (0, 1, and -1)

CUTEly MAD is a curated dataset for document-level sentiment analysis in the Malayalam language. CUTEly MAD is short for Curated Twitter Malayalam Dataset. The dataset is created by extracting Malayalam tweets from Twitter. A set of both positive and negative sentiment-oriented Malayalam words are identified, which are used as hashtags to extract tweets using Twitter API. Further, these tweets were manually labeled by a proficient annotator, based on their sentiment polarity into two classes, viz. negative and positive. If the sentiment is positive, then 1 is annotated. Otherwise, 0 is labeled for negative sentiment. A total of 2,000 tweets are labeled, where 50% are positive tweets and the other 50% are negative sentiment oriented.

TweetSentimentExtractionClassification An MTEB dataset Massive Text Embedding Benchmark

Task category t2c

Domains Social, Written

Reference https://www.kaggle.com/competitions/tweet-sentiment-extraction/overview

  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(["TweetSentimentExtractionClassification"]) evaluator = mteb.MTEB(task)

model =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/tweet_sentiment_extraction.

This dataset is a large-scale collection of Twitter messages used for sentiment analysis tasks. It consists of millions of tweets labeled with sentiments to understand public emotions expressed online. The dataset provides real-time insights into how users react to various events across domains like politics, healthcare, and entertainment. Tweets are drawn from diverse users, timestamps, and queries, offering a rich context for text-based emotion analysis. The sentiments are categorized into negative, neutral, and positive classes. It serves as a benchmark for evaluating natural language processing techniques on short and informal social media content.

Twitter Sentiment Analysis: Prabowo's First 100 Days

  Dataset Overview

This dataset contains tweets related to President Prabowo Subianto's first 100 days in office in Indonesia (2024-2029). The tweets have been preprocessed and classified into three sentiment categories using a fine-tuned BERT model for Indonesian language (IndoBERT).

  Dataset Details

Language: Indonesian Source: Twitter/X Time period: First 100 days of President Prabowo's… See the full description on the dataset page: https://huggingface.co/datasets/KidzRizal/twitter-sentiment-analysis.

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.