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.

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.

This dataset page is currently being updated. The tweets collected by the model deployed at https://live.rlamsal.com.np/ are shared here. However, because of COVID-19, all computing resources I have are being used for a dedicated collection of the tweets related to the pandemic. You can go through the following datasets to access those tweets:Coronavirus (COVID-19) Tweets Dataset: https://ieee-dataport.org/open-access/coronavirus-covid-19-tweets-datasetCoronavirus (COVID-19) Geo-tagged Tweets Dataset: https://ieee-dataport.org/open-access/coronavirus-covid-19-geo-tagged-tweets-dataset

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"). You can download the non-aggregated results (55,000 rows) here.

Source: https://www.crowdflower.com/data-for-everyone/

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

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.

A look into the sentiment around Apple, based on tweets containing #AAPL, @apple, etc. Contributors were given a tweet and asked whether the user was positive, negative, or neutral about Apple. (They were also allowed to mark "the tweet is not about the company Apple, Inc.)

Source: https://www.crowdflower.com/data-for-everyone/

The dataset contains over 1.6 million tweets (tweet IDs), labeled with sentiment by human annotators. There are 15 Twitter corpora for the corresponding 15 European languages. The data can be used to train and evaluate Twitter sentiment classifiers, to compute annotator agreement, or to study the differences between language usage on Twitter.

The data analysis is described in the following papers:

I. Mozetič, M. Grčar, J. Smailović. Multilingual Twitter sentiment classification: The role of human annotators, PLoS ONE 11(5): e0155036, doi: 10.1371/journal.pone.e0155036, 2016. (http://dx.doi.org/10.1371/journal.pone.0155036)

I. Mozetič, L. Torgo, V. Cerqueira, J. Smailović. How to evaluate sentiment classifiers for Twitter time-ordered data?, PLoS ONE 13(3): e0194317, doi: 10.1371/journal.pone.0194317, 2018. (https://dx.doi.org/10.1371/journal.pone.0194317)

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.

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.

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.”.

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.

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

3 million tweets containing both text and images

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.

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

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.

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)

Dataset

This dataset was created by Mennatullah ELsahy

Contents

Arabic Sentiment Tweets Dataset (ASTD) is an Arabic social sentiment analysis dataset gathered from Twitter. It consists of about 10,000 tweets which are classified as objective, subjective positive, subjective negative, and subjective mixed.