because of COVID-19

This is an entity-level Twitter Sentiment Analysis dataset. For each message, the task is to judge the sentiment of the entire sentence towards a given entity. For example, A outperforms B is positive for entity A but negative for entity B. The dataset contains ~70K labeled training messages and 1K labeled validation messages. It is available online for free on Kaggle.

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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.

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

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)

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.

SSH CENTRE (Social Sciences and Humanities for Climate, Energy aNd Transport Research Excellence) is a Horizon Europe project, engaging directly with stakeholders across research, policy, and business (including citizens) to strengthen social innovation, SSH-STEM collaboration, transdisciplinary policy advice, inclusive engagement, and SSH communities across Europe, accelerating the EU’s transition to carbon neutrality. SSH CENTRE is based in a range of activities related to Open Science, inclusivity and diversity – especially with regards Southern and Eastern Europe and different career stages – including: development of novel SSH-STEM collaborations to facilitate the delivery of the EU Green Deal; SSH knowledge brokerage to support regions in transition; and the effective design of strategies for citizen engagement in EU R&I activities. Outputs include action-led agendas and building stakeholder synergies through regular Policy Insight events.This is captured in a high-profile virtual SSH CENTRE generating and sharing best practice for SSH policy advice, overcoming fragmentation to accelerate the EU’s journey to a sustainable future.The documents uploaded here are part of WP2 whereby novel, interdisciplinary teams were provided funding to undertake activities to develop a policy recommendation related to EU Green Deal policy. Each of these policy recommendations, and the activities that inform them, will be written-up as a chapter in an edited book collection. Three books will make up this edited collection - one on climate, one on energy and one on mobility. As part of writing a chapter for the SSH CENTRE book on ‘Mobility’, we set out to analyse the sentiment of users on Twitter regarding shared and active mobility modes in Brussels. This involved us collecting tweets between 2017-2022. A tweet was collected if it contained a previously defined mobility keyword (for example: metro) and either the name of a (local) politician, a neighbourhood or municipality, or a (shared) mobility provider. The files attached to this Zenodo webpage is a csv files containing the tweets collected.”.

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 excel work book includes NRC sentiment analysis for all hashtags, #pride tweets, #lesbian tweets, #pride NRC scores, # lesbian NRC scores, all sentiment scores in the syuzhet package for #pride and lesbian, lexicon comparison, #lesbian subsamples and #pride subsamples.

This dataset provides a collection of 1000 tweets designed for sentiment analysis. The tweets were sourced from Twitter using Python and systematically generated using various modules to ensure a balanced representation of different tweet types, user behaviours, and sentiments. This includes the use of a random module for IDs and text, a faker module for usernames and dates, and a textblob module for assigning sentiment. The dataset's purpose is to offer a robust foundation for analysing and visualising sentiment trends and patterns, aiding in the initial exploration of data and the identification of significant patterns or trends.

Columns

• Tweet ID: A unique identifier assigned to each individual tweet.
• Text: The actual textual content of the tweet.
• User: The username of the individual who posted the tweet.
• Created At: The date and time when the tweet was originally published.
• Likes: The total number of likes or approvals the tweet received.
• Retweets: The total count of times the tweet was shared by other users.
• Sentiment: The categorised emotional tone of the tweet, typically labelled as positive, neutral, or negative.

Distribution

The dataset is provided in a CSV file format. It consists of 1000 individual tweet records, structured in a tabular layout with the columns detailed above. A sample file will be made available separately on the platform.

Usage

This dataset is ideal for: * Analysing and visualising sentiment trends and patterns in social media. * Initial data exploration to uncover insights into tweet characteristics and user emotions. * Identifying underlying patterns or trends within social media conversations. * Developing and training machine learning models for sentiment classification. * Academic research into Natural Language Processing (NLP) and social media dynamics. * Educational purposes, allowing students to practise data analysis and visualisation techniques.

Coverage

The dataset spans tweets created between January and April 2023, as observed from the included data samples. While specific geographic or demographic information for users is not available within the dataset, the nature of Twitter implies a general global scope, reflecting a variety of user behaviours and sentiments without specific regional or population group focus.

License

CC0

Who Can Use It

This dataset is valuable for: * Data Scientists and Machine Learning Engineers working on NLP tasks and model development. * Researchers in fields such as Natural Language Processing, Machine Learning Algorithms, Deep Learning, and Computer Science. * Data Analysts looking to extract insights from social media content. * Academics and Students undertaking projects related to sentiment analysis or social media studies. * Anyone interested in understanding online sentiment and user behaviour on social media platforms.

Dataset Name Suggestions

• Twitter Public Sentiment Dataset
• Social Media Text Sentiment Analysis
• General Tweet Mood Data
• Twitter Sentiment Collection 2023
• Microblog Sentiment Dataset

Attributes

Original Data Source: Twitter Sentiment Analysis using Roberta and VaderTwitter Sentiment Analysis using Roberta and Vader

Twitter Sentiment Analysis Dataset

Overview

This is an entity-level sentiment analysis dataset of twitter. Given a message and an entity, the task is to judge the sentiment of the message about the entity. There are three classes in this dataset: Positive, Negative and Neutral. We regard messages that are not relevant to the entity (i.e. Irrelevant) as Neutral.

Usage

Please use twitter_training.csv as the training set and twitter_validation.csv as the validation set. Top 1 classification accuracy is used as the metric.

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.

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.

1) Data Introduction • The Sentiment Analysis Dataset is a dataset for emotional analysis, including large-scale tweet text collected from Twitter and emotional polarity (0=negative, 2=neutral, 4=positive) labels for each tweet, featuring automatic labeling based on emoticons.

2) Data Utilization (1) Sentiment Analysis Dataset has characteristics that: • Each sample consists of six columns: emotional polarity, tweet ID, date of writing, search word, author, and tweet body, and is suitable for training natural language processing and classification models using tweet text and emotion labels. (2) Sentiment Analysis Dataset can be used to: • Emotional Classification Model Development: Using tweet text and emotional polarity labels, we can build positive, negative, and neutral emotional automatic classification models with various machine learning and deep learning models such as logistic regression, SVM, RNN, and LSTM. • Analysis of SNS public opinion and trends: By analyzing the distribution of emotions by time series and keywords, you can explore changes in public opinion on specific issues or brands, positive and negative trends, and key emotional keywords.

This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple peaks and drops that need further analysis. The n-grams during those peaks and drops can prove beneficial for better understanding the discourse. The dataset will be updated weekly and will continue until the development of the Coronavirus (COVID-19) Tweets Dataset is ongoing.

This dataset provides a collection of tweets, each categorised by its sentiment. It is designed to assist in developing and evaluating machine learning models, particularly for natural language processing tasks. The primary aim is to distinguish between different sentiments expressed in tweets, helping to address issues like harmful content by enabling the creation of robust classifier models. Each entry includes the tweet text and its corresponding sentiment label, with a specific focus on identifying the exact word or phrase within the tweet that encapsulates that sentiment.

Columns

• textID: A unique identifier for each tweet entry.
• text: The full content of the tweet.
• selected_text: The specific part of the tweet that best represents the given sentiment.
• sentiment: The overall sentiment expressed in the tweet, categorised as neutral, positive, or other.

Distribution

The dataset contains approximately 27,500 tweets. It is typically provided in a CSV file format. The textID and text columns each contain 27,481 unique values, while the selected_text column has 22,464 unique values. The sentiment distribution is as follows: 40% are neutral, 31% are positive, and 28% fall into other sentiment categories. When processing the data from the CSV, it is important to remove any beginning or ending quotation marks from the text fields.

Usage

This dataset is ideally suited for tasks involving sentiment analysis and text classification. It can be used to build and train classification models that predict the sentiment of Twitter tweets. Furthermore, it allows for the comparison and evaluation of various classification algorithms based on their performance metrics in predicting sentiments. It is particularly useful for developing strong NLP-based classifier models to identify and categorise tweets by sentiment.

Coverage

The data originates from a global platform, Twitter, and the sentiment analysis is applicable across a wide range of content. The dataset's structure allows for analysis of sentiments in tweets, covering various topics and expressions globally. No specific time range or demographic scope is detailed beyond its global applicability.

License

CCO

Who Can Use It

This dataset is suitable for a diverse range of users, including beginners in data science and machine learning. It is especially beneficial for those interested in social network analysis, text classification, and natural language processing. Intended users include data scientists, researchers, and developers looking to build and test models for predicting social media sentiments or for applications like content moderation.

Dataset Name Suggestions

• Twitter Tweet Sentiment Dataset
• Tweet Sentiment Analysis Dataset
• Social Media Sentiment Prediction Data
• Twitter Sentiment Classification Data

Attributes

Original Data Source: Twitter Tweets Sentiment Dataset

This database contains training, validation, and test sets created for a Twitter sentiment classification project. The tweets were cleaned and improved with custom-calculated sentiment scores and magnitudes using a word-weighted dictionary. The data is split to support machine learning experiments.

1) Data introduction • Twitter-tweets-sentiment dataset is a dataset that aims to analyze tweet sentiment for Twitter and natural language processing.

2) Data utilization (1)Twitter-tweets-sentiment data has characteristics that: • The data consists of three columns, including emotion and text, and aims to block negative tweets through a powerful classification model. (2) Twitter-tweets-sentiment data can be used to: • Social Media Monitoring: Businesses and organizations can use data to monitor social media platforms and gauge public sentiment about a brand, product, event, or social issue. • Sentiment analysis: This dataset can be used to train models that classify the sentiment of tweets, which can help companies and researchers understand public opinion on a variety of topics.

If you use the dataset, cite the paper: https://doi.org/10.1016/j.eswa.2022.117541

The most comprehensive dataset to date regarding climate change and human opinions via Twitter. It has the heftiest temporal coverage, spanning over 13 years, includes over 15 million tweets spatially distributed across the world, and provides the geolocation of most tweets. Seven dimensions of information are tied to each tweet, namely geolocation, user gender, climate change stance and sentiment, aggressiveness, deviations from historic temperature, and topic modeling, while accompanied by environmental disaster events information. These dimensions were produced by testing and evaluating a plethora of state-of-the-art machine learning algorithms and methods, both supervised and unsupervised, including BERT, RNN, LSTM, CNN, SVM, Naive Bayes, VADER, Textblob, Flair, and LDA.

The following columns are in the dataset:

➡ created_at: The timestamp of the tweet. ➡ id: The unique id of the tweet. ➡ lng: The longitude the tweet was written. ➡ lat: The latitude the tweet was written. ➡ topic: Categorization of the tweet in one of ten topics namely, seriousness of gas emissions, importance of human intervention, global stance, significance of pollution awareness events, weather extremes, impact of resource overconsumption, Donald Trump versus science, ideological positions on global warming, politics, and undefined. ➡ sentiment: A score on a continuous scale. This scale ranges from -1 to 1 with values closer to 1 being translated to positive sentiment, values closer to -1 representing a negative sentiment while values close to 0 depicting no sentiment or being neutral. ➡ stance: That is if the tweet supports the belief of man-made climate change (believer), if the tweet does not believe in man-made climate change (denier), and if the tweet neither supports nor refuses the belief of man-made climate change (neutral). ➡ gender: Whether the user that made the tweet is male, female, or undefined. ➡ temperature_avg: The temperature deviation in Celsius and relative to the January 1951-December 1980 average at the time and place the tweet was written. ➡ aggressiveness: That is if the tweet contains aggressive language or not.

Since Twitter forbids making public the text of the tweets, in order to retrieve it you need to do a process called hydrating. Tools such as Twarc or Hydrator can be used to hydrate tweets.

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.