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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 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 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%)

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.

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

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

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

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.

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.

Financial Sentiment Analysis Dataset

  Overview

This dataset is a comprehensive collection of tweets focused on financial topics, meticulously curated to assist in sentiment analysis in the domain of finance and stock markets. It serves as a valuable resource for training machine learning models to understand and predict sentiment trends based on social media discourse, particularly within the financial sector.

  Data Description

The dataset comprises tweets… See the full description on the dataset page: https://huggingface.co/datasets/TimKoornstra/financial-tweets-sentiment.

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)

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)

Evaluation of Aspect-Based Sentiment Analysis performance on the test split of the Twitter dataset, measured by Accuracy and F1 score.

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.

Dataset Card for tweet_eval

  Dataset Summary

TweetEval consists of seven heterogenous tasks in Twitter, all framed as multi-class tweet classification. The tasks include - irony, hate, offensive, stance, emoji, emotion, and sentiment. All tasks have been unified into the same benchmark, with each dataset presented in the same format and with fixed training, validation and test splits.

  Supported Tasks and Leaderboards

text_classification: The dataset can be… See the full description on the dataset page: https://huggingface.co/datasets/cardiffnlp/tweet_eval.

Evaluation of Aspect-Based Sentiment Analysis performance on the Twitter dataset, measured by Accuracy and Macro-F1 score.

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. 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. 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.