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Description:

Twitter is an online Social Media Platform where people share their their though as tweets. It is observed that some people misuse it to tweet hateful content. Twitter is trying to tackle this problem and we shall help it by creating a strong NLP based-classifier model to distinguish the negative tweets & block such tweets. Can you build a strong classifier model to predict the same?

Each row contains the text of a tweet and a sentiment label. In the training set you are provided with a word or phrase drawn from the tweet (selected_text) that encapsulates the provided sentiment.

Make sure, when parsing the CSV, to remove the beginning / ending quotes from the text field, to ensure that you don't include them in your training.

You're attempting to predict the word or phrase from the tweet that exemplifies the provided sentiment. The word or phrase should include all characters within that span (i.e. including commas, spaces, etc.)

Columns:

1. textID - unique ID for each piece of text
2. text - the text of the tweet
3. sentiment - the general sentiment of the tweet

Acknowledgement:

The dataset is download from Kaggle Competetions:
https://www.kaggle.com/c/tweet-sentiment-extraction/data?select=train.csv

Objective:

• Understand the Dataset & cleanup (if required).
• Build classification models to predict the twitter sentiments.
• Compare the evaluation metrics of vaious classification algorithms.

This dataset contains a collection of tweets from the Indonesian community, expressing their opinions on the government's implementation of PPKM (Enforcement of Community Activity Restrictions). The dataset consists of approximately 20,000 tweets gathered within the time range from April 1, 2020, to April 1, 2022.

The selected time range for data collection is based on when Indonesia started implementing PPKM extensively and when the government revoked the policy. Within this dataset, diverse opinions, comments, and reactions from the public regarding the PPKM policy during that period can be found.

This dataset provides an opportunity to analyze the sentiment and public views regarding the PPKM policy, as well as observe changes in opinions over time. It offers valuable insights into understanding the perceptions and reactions of the community towards government policies related to PPKM.

Label: 0 (Positive), 1 (Neutral), 2 (Negative)

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.

Dataset description Users assessed tweets related to various brands and products, providing evaluations on whether the sentiment conveyed was positive, negative, or neutral. Additionally, if the tweet conveyed any sentiment, contributors identified the specific brand or product targeted by that emotion.

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Train Dataset : 8589 rows x 3 columns https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F11965067%2Fe998ba81ca461699a787ff7305486b24%2FTrainDS.JPG?generation=1704672608361793&alt=media" alt="">

Test Dataset : 504 rows x 1 columns https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F11965067%2F07df18965e91f84df123270aabb641e1%2Ftest.JPG?generation=1704679582009718&alt=media" alt="">

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.

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.

📊 Dataset Description

The Twitter Sentiment Analysis dataset contains 74,681 records with information about different Twitter posts. Each record includes a unique ID, the related entity/topic, a sentiment label, and the tweet text.

The dataset contains four sentiment categories: Positive, Negative, Neutral, and Irrelevant. It can be used for Natural Language Processing (NLP) and sentiment classification tasks, where the goal is to predict the sentiment expressed in a given tweet.

Dataset Features

• ID – Unique identifier for each record.
• Entity – The topic or entity associated with the tweet.
• Sentiment – Sentiment category of the tweet.
• Tweet – The actual text of the Twitter post.

Overall, this dataset provides a useful foundation for developing and evaluating Twitter sentiment classification models using Machine Learning and Deep Learning techniques.

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

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.

Dataset

This dataset was created by Dr. Zohair Ahmed

Contents

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)

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.

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.

Wider spatiotemporal English COVID-19 Tweets

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.

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.

Sentiment140 allows you to discover the sentiment of a brand, product, or topic on Twitter.

The data is a CSV with emoticons removed. Data file format has 6 fields:

1. the polarity of the tweet (0 = negative, 2 = neutral, 4 = positive)
2. the id of the tweet (2087)
3. the date of the tweet (Sat May 16 23:58:44 UTC 2009)
4. the query (lyx). If there is no query, then this value is NO_QUERY.
5. the user that tweeted (robotickilldozr)
6. the text of the tweet (Lyx is cool)

For more information, refer to the paper Twitter Sentiment Classification with Distant Supervision at https://cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('sentiment140', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

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.