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

The Twitter Sentiment Analysis Dataset contains 1,578,627 classified tweets, each row is marked as 1 for positive sentiment and 0 for negative sentiment. The dataset is based on data from the following two sources:

University of Michigan Sentiment Analysis competition on Kaggle Twitter Sentiment Corpus by Niek Sanders

Finally, I randomly selected a subset of them, applied a cleaning process, and divided them between the test and train subsets, keeping a balance between the number of positive and negative tweets within each of these subsets.

🐦 Twitter Sentiment Analysis (bdstar/twitter-sentiment-analysis)

  🧠 Overview

A refined and merged version of Twitter text sentiment datasets, providing a clean and well-balanced dataset for sentiment classification across three sentiment categories:positive, negative, and neutral. This dataset is split into three parts — train, test, and validation — each sourced from highly reputable open datasets.It is designed for training, evaluating, and benchmarking NLP… See the full description on the dataset page: https://huggingface.co/datasets/akhiljoe143/twitter-sentiment-analysis.

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.

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")

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.

The dataset includes key attributes such as Tweet ID, Username, Tweet Text, Retweets, Favorites, Followers, Timestamp, and Sentiment. Each entry is generated using the Faker library, ensuring the diversity and randomness of the data while preserving the structural integrity of real-world Twitter data.

The 'Sentiment' column represents the emotional tone associated with each tweet and is categorized into 'Positive', 'Negative', or 'Neutral' sentiments. This categorization is randomly assigned, emulating the dynamic nature of sentiment in social media content.

Researchers, data scientists, and machine learning practitioners can leverage this synthetic dataset to develop and test sentiment analysis models, explore feature engineering techniques, and evaluate the performance of algorithms in a controlled environment. The dataset serves as a valuable resource for honing natural language processing skills and gaining insights into sentiment trends in social media data. While synthetic, it mirrors the complexities and nuances found in real Twitter data, providing a foundation for robust sentiment analysis research and experimentation.

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%)

Re-uploading this data raw as the other dataset seems to have less tweets and were edited

Labels are 1 Negative 3 Neutral 5 Positive

Data from data.world

Twitter Sentiment Dataset

Sample English-only tweet sentiment dataset. Each row represents a single tweet with anonymized text and conversation structure. This is a sample dataset. To access the full version or request any custom dataset tailored to your needs, contact DataHive at contact@datahive.ai.

  Files Included

dataset.csv – tweets data

  What’s included

Anonymized tweet text Conversation linkage via root_id and parent_id 3-class sentiment… See the full description on the dataset page: https://huggingface.co/datasets/datahiveai/Twitter-Conversations-Sentiment-Dataset.

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.

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… See the full description on the dataset page: https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment.

Unleash the Power of Apple Sentiment Analysis: A High-Quality, Labeled Tweet Dataset

Struggling to find the perfect dataset for fine-tuning your Transformer model on Apple sentiment analysis? Look no further! Tired of dealing with scattered, low-quality data on Kaggle? We've been there too. That's why we created this comprehensive dataset, meticulously crafted to meet your needs.

What makes this dataset unique?

• Extensive Collection: Merges various Kaggle datasets to deliver a substantial volume of tweets (5810 tweets), exceeding the limitations of individual sources.
• Rigorous Cleaning: Eliminates spam, irrelevant content, and misleading links, ensuring data integrity and model accuracy.
• Clear Labeling: Every tweet is meticulously labeled with positive, negative, or neutral sentiment, ready for immediate training.

No more piecing together fragmented datasets or battling with unreliable information. This pre-processed and labeled collection empowers you to:

• Develop superior sentiment analysis models for understanding Apple brand perception.
• Gain valuable insights into customer opinions and preferences.
• Accurately track sentiment trends over time.
• Fine-tune your Transformer model with confidence and efficiency.(BERT, RoBERTa, DistilBERT etc.)

Dataset Card for "Large twitter tweets sentiment analysis"

  Dataset Description







  Dataset Summary

This dataset is a collection of tweets formatted in a tabular data structure, annotated for sentiment analysis. Each tweet is associated with a sentiment label, with 1 indicating a Positive sentiment and 0 for a Negative sentiment.

  Languages

The tweets in English.

  Dataset Structure







  Data Instances

An instance of… See the full description on the dataset page: https://huggingface.co/datasets/gxb912/large-twitter-tweets-sentiment.

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.

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.

Twitter Sentiment Meta-Analysis Dataset

  Dataset Description

This dataset contains sentiment analysis results for English tweets collected between September 2009 and January 2010. The tweets were processed and analyzed using 10 different sentiment classifiers, with the final sentiment score derived from principal component analysis (PCA).

  Source Data

Original Data: Cheng-Caverlee-Lee Twitter Scrape (Sept 2009 - Jan 2010) Number of Tweets: 138 690… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/twitter-sentiment-meta-analysis.

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.

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.

We introduce the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in NigeriaHausa, Igbo, Nigerian-Pidgin, and Yorbconsisting of around 30,000 annotated tweets per language (except for Nigerian-Pidgin), including a significant fraction of code-mixed tweets.