https://raw.githubusercontent.com/Masterx-AI/Project_Twitter_Sentiment_Analysis_/main/twitt.jpg" alt="">

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)

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="">

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.

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.

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

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 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.

Description

Our dataset comprises 1000 tweets, which were taken from Twitter using the Python programming language. The dataset was stored in a CSV file and generated using various modules. The random module was used to generate random IDs and text, while the faker module was used to generate random user names and dates. Additionally, the textblob module was used to assign a random sentiment to each tweet.

This systematic approach ensures that the dataset is well-balanced and represents different types of tweets, user behavior, and sentiment. It is essential to have a balanced dataset to ensure that the analysis and visualization of the dataset are accurate and reliable. By generating tweets with a range of sentiments, we have created a diverse dataset that can be used to analyze and visualize sentiment trends and patterns.

In addition to generating the tweets, we have also prepared a visual representation of the data sets. This visualization provides an overview of the key features of the dataset, such as the frequency distribution of the different sentiment categories, the distribution of tweets over time, and the user names associated with the tweets. This visualization will aid in the initial exploration of the dataset and enable us to identify any patterns or trends that may be present.

Categories

Natural Language Processing, Machine Learning Algorithm, Deep Learning

Acknowledgements & Source

Jannatul Ferdoshi

Institutions: BRAC University

Data Source

Image Source:Twitter Sentiment Analysis Using Python GeeksforGeeks | lacienciadelcafe.com.ar

Please don't forget to upvote if you find this useful.

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

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.

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.

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).

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)

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.

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.

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.

Wider spatiotemporal English COVID-19 Tweets