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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.

Social network X/Twitter is particularly popular in the United States, and as of February 2025, the microblogging service had an audience reach of 103.9 million users in the country. Japan and the India were ranked second and third with more than 70 million and 25 million users respectively. Global Twitter usage As of the second quarter of 2021, X/Twitter had 206 million monetizable daily active users worldwide. The most-followed Twitter accounts include figures such as Elon Musk, Justin Bieber and former U.S. president Barack Obama. X/Twitter and politics X/Twitter has become an increasingly relevant tool in domestic and international politics. The platform has become a way to promote policies and interact with citizens and other officials, and most world leaders and foreign ministries have an official Twitter account. Former U.S. president Donald Trump used to be a prolific Twitter user before the platform permanently suspended his account in January 2021. During an August 2018 survey, 61 percent of respondents stated that Trump's use of Twitter as President of the United States was inappropriate.

Advertising makes up 89% of its total revenue and data licensing makes up about 11%.

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)

These are the key Twitter user statistics that you need to know.

The number of Twitter users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 0.9 million users (+5.1 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 18.55 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The number of Twitter users in the United States was forecast to continuously increase between 2024 and 2028 by in total 4.3 million users (+5.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 85.08 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like Canada and Mexico.

Please cite the following paper when using this dataset: N. Thakur, “Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions,” Preprints, 2022, DOI: 10.20944/preprints202206.0383.v1 Abstract The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and use cases in assisted living, military, healthcare, firefighting, and industries. With the projected increase in the diverse uses of exoskeletons in the next few years in these application domains and beyond, it is crucial to study, interpret, and analyze user perspectives, public opinion, reviews, and feedback related to exoskeletons, for which a dataset is necessary. The Internet of Everything era of today's living, characterized by people spending more time on the Internet than ever before, holds the potential for developing such a dataset by mining relevant web behavior data from social media communications, which have increased exponentially in the last few years. Twitter, one such social media platform, is highly popular amongst all age groups, who communicate on diverse topics including but not limited to news, current events, politics, emerging technologies, family, relationships, and career opportunities, via tweets, while sharing their views, opinions, perspectives, and feedback towards the same. Therefore, this work presents a dataset of about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons. Instructions: This dataset contains about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons. The dataset contains only tweet identifiers (Tweet IDs) due to the terms and conditions of Twitter to re-distribute Twitter data only for research purposes. They need to be hydrated to be used. The process of retrieving a tweet's complete information (such as the text of the tweet, username, user ID, date and time, etc.) using its ID is known as the hydration of a tweet ID. The Hydrator application (link to download the application: https://github.com/DocNow/hydrator/releases and link to a step-by-step tutorial: https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets) or any similar application may be used for hydrating this dataset. Data Description This dataset consists of 7 .txt files. The following shows the number of Tweet IDs and the date range (of the associated tweets) in each of these files. Filename: Exoskeleton_TweetIDs_Set1.txt (Number of Tweet IDs – 22945, Date Range of Tweets - July 20, 2021 – May 21, 2022) Filename: Exoskeleton_TweetIDs_Set2.txt (Number of Tweet IDs – 19416, Date Range of Tweets - Dec 1, 2020 – July 19, 2021) Filename: Exoskeleton_TweetIDs_Set3.txt (Number of Tweet IDs – 16673, Date Range of Tweets - April 29, 2020 - Nov 30, 2020) Filename: Exoskeleton_TweetIDs_Set4.txt (Number of Tweet IDs – 16208, Date Range of Tweets - Oct 5, 2019 - Apr 28, 2020) Filename: Exoskeleton_TweetIDs_Set5.txt (Number of Tweet IDs – 17983, Date Range of Tweets - Feb 13, 2019 - Oct 4, 2019) Filename: Exoskeleton_TweetIDs_Set6.txt (Number of Tweet IDs – 34009, Date Range of Tweets - Nov 9, 2017 - Feb 12, 2019) Filename: Exoskeleton_TweetIDs_Set7.txt (Number of Tweet IDs – 11351, Date Range of Tweets - May 21, 2017 - Nov 8, 2017) Here, the last date for May is May 21 as it was the most recent date at the time of data collection. The dataset would be updated soon to incorporate more recent tweets.

This dataset comprises a set of Twitter accounts in Singapore that are used for social bot profiling research conducted by the Living Analytics Research Centre (LARC) at Singapore Management University (SMU). Here a bot is defined as a Twitter account that generates contents and/or interacts with other users automatically (at least according to human judgment). In this research, Twitter bots have been categorized into three major types:

Broadcast bot. This bot aims at disseminating information to general audience by providing, e.g., benign links to news, blogs or sites. Such bot is often managed by an organization or a group of people (e.g., bloggers). Consumption bot. The main purpose of this bot is to aggregate contents from various sources and/or provide update services (e.g., horoscope reading, weather update) for personal consumption or use. Spam bot. This type of bots posts malicious contents (e.g., to trick people by hijacking certain account or redirecting them to malicious sites), or promotes harmless but invalid/irrelevant contents aggressively.

This categorization is general enough to cater for new, emerging types of bot (e.g., chatbots can be viewed as a special type of broadcast bots). The dataset was collected from 1 January to 30 April 2014 via the Twitter REST and streaming APIs. Starting from popular seed users (i.e., users having many followers), their follow, retweet, and user mention links were crawled. The data collection proceeds by adding those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. Using this procedure, a total of 159,724 accounts have been collected. To identify bots, the first step is to check active accounts who tweeted at least 15 times within the month of April 2014. These accounts were then manually checked and labelled, of which 589 bots were found. As many more human users are expected in the Twitter population, the remaining accounts were randomly sampled and manually checked. With this, 1,024 human accounts were identified. In total, this results in 1,613 labelled accounts. Related Publication: R. J. Oentaryo, A. Murdopo, P. K. Prasetyo, and E.-P. Lim. (2016). On profiling bots in social media. Proceedings of the International Conference on Social Informatics (SocInfo’16), 92-109. Bellevue, WA. https://doi.org/10.1007/978-3-319-47880-7_6

One of the biggest advantages of Twitter is the speed at which information can be passed around. People use Twitter primarily to get news and for entertainment. This is the breakdown of why people use Twitter today.

Profile growth - the growth on our social platforms to see where and when we're gaining followers. Engagement rate - a ratio of how many people interacted with ours posts based on when users are usually online. Reach - the number of feeds our posts appeared in (doesn't mean people interacted with the post).

As of December 2022, X/Twitter's audience accounted for over *** million monthly active users worldwide. This figure was projected to ******** to approximately *** million by 2024, a ******* of around **** percent compared to 2022.

The US has historically been the target country for Twitter since its launch in 2006. This is the full breakdown of Twitter users by country.

Dengue is a mosquito-borne viral disease which infects millions of people every year, specially in developing countries. Some of the main challenges facing the disease are reporting risk indicators and rapidly detecting outbreaks. Traditional surveillance systems rely on passive reporting from health-care facilities, often ignoring human mobility and locating each individual by their home address. Yet, geolocated data are becoming commonplace in social media, which is widely used as means to discuss a large variety of health topics, including the users' health status. In this dataset paper, we make available two large collections of dengue related labeled Twitter data. One is a set of tweets available through the Streaming API using the keywords dengue and aedes from 2010 to 2016. The other is the set of all geolocated tweets in Brazil during the year of 2015 (available also through the Streaming API). We detail the process of collecting and labeling each tweet containing keywords related to dengue in one of 5 categories: personal experience, information, opinion, campaign, and joke. This dataset can be useful for the development of models for spatial disease surveillance, but also scenarios such as understanding health-related content in a language other than English, and studying human mobility.

Context

Scrapped from twitters from 2016-01-01 to 2019-03-29, Collecting Tweets containing Bitcoin or BTC Tools used:

Twint Tweepy

Content

Tweet in multiple Language & Talked about Bitcoin

Acknowledgements

Thanks to Alex ( https://www.kaggle.com/alaix14 ) for his dataset (https://www.kaggle.com/alaix14/bitcoin-tweets-20160101-to-20190329 ), It is just an additional dimension where Sentiment is analyzed with a price change for Bitcoin

The platform is male-dominated with 68.1% of all Twitter users being male. Just 31.9% of Twitter users are female.

The dataset is about public conversation on Twitter surrounding the COVID-19 pandemic. They annotated seventeen latent semantic attributes for each public tweet using natural language processing techniques and machine-learning based algorithms. The latent semantic attributes include: 1) ten attributes indicating the tweet’s relevance to ten detected topics, 2) five quantitative attributes indicating the degree of intensity in the valence (i.e., unpleasantness/pleasantness) and emotional intensities across four primary emotions of fear, anger, sadness and joy, and 3) two qualitative attributes indicating the sentiment category and the most dominant emotion category, respectively. Data is accessible to people who have an OPEN ICPSR account.

This is the dataset for the article "A Predictive Method to Improve the Effectiveness of Twitter Communication in a Cultural Heritage Scenario".

Abstract:

Museums are embracing social technologies in the attempt to broaden their audience and to engage people. Although social communication seems an easy task, media managers know how hard it is to reach millions of people with a simple message. Indeed, millions of posts are competing every day to get visibility in terms of likes and shares and very little research focused on museums communication to identify best practices. In this paper, we focus on Twitter and we propose a novel method that exploits interpretable machine learning techniques to: (a) predict whether a tweet will likely be appreciated by Twitter users or not; (b) present simple suggestions that will help enhancing the message and increasing the probability of its success. Using a real-world dataset of around 40,000 tweets written by 23 world famous museums, we show that our proposed method allows identifying tweet features that are more likely to influence the tweet success.

Code to run a selection of experiments is available at https://github.com/rmartoglia/predict-twitter-ch

Dataset structure

The dataset contains the dataset used in the experiments of the above research paper. Only the extracted features for the museum tweet threads (and not the message full text) are provided and needed for the analyses.

We selected 23 well known world spread art museums and grouped them into five groups: G1 (museums with at least three million of followers); G2 (museums with more than one million of followers); G3 (museums with more than 400,000 followers); G4 (museums with more that 200,000 followers); G5 (Italian museums). From these museums, we analyzed ca. 40,000 tweets, with a number varying from 5k ca. to 11k ca. for each museum group, depending on the number of museums in each group.

Content features: these are the features that can be drawn form the content of the tweet itself. We further divide such features in the following two categories:

– Countable: these features have a value ranging into different intervals. We take into consideration: the number of hashtags (i.e., words preceded by #) in the tweet, the number of URLs (i.e., links to external resources), the number of images (e.g., photos and graphical emoticons), the number of mentions (i.e., twitter accounts preceded by @), the length of the tweet;

– On-Off : these features have binary values in {0, 1}. We observe whether the tweet has exclamation marks, question marks, person names, place names, organization names, other names. Moreover, we also take into consideration the tweet topic density: assuming that the involved topics correspond to the hashtags mentioned in the text, we define a tweet as dense of topics if the number of hashtags it contains is greater than a given threshold, set to 5. Finally, we observe the tweet sentiment that might be present (positive or negative) or not (neutral).

Context features: these features are not drawn form the content of the tweet itself and might give a larger picture of the context in which the tweet was sent. Namely, we take into consideration the part of the day in which the tweet was sent (morning, afternoon, evening and night respectively from 5:00am to 11:59am, from 12:00pm to 5:59pm, from 6:00pm to 10:59pm and from 11pm to 4:59am), and a boolean feature indicating whether the tweet is a retweet or not.

User features: these features are proper of the user that sent the tweet, and are the same for all the tweets of this user. Namely we consider the name of the museum and the number of followers of the user.

This is the breakdown of Twitter users by age group.

Context:

There are lots of really cool datasets getting added to Kaggle every day, and as part of my job I want to help people find them. I’ve been tweeting about datasets on my personal Twitter accounts @rctatman and also releasing a weekly newsletter of interesting datasets.

I wanted to know which method was more effective at getting the word out about new datasets: Twitter or the newsletter?

Content:

This dataset contains two .csv files. One has information on the impact of tweets with links to datasets, while the other has information on the impact of the newsletter.

Twitter:

The Twitter .csv has the following information:

• month: The month of the tweet (1-12)
• day: The day of the tweet (1-31)
• hour: The hour of the tweet (1-24)
• impressions: The number of impressions the tweet got
• engagement: The number of total engagements
• clicks: The number of URL clicks

Fridata Newsletter:

The Fridata .csv has the following information:

• date: The Date the newsletter was sent out
• month: The Month the newsletter was sent out (1-12)
• day: The day the newsletter was sent out (1-31)
• # of dataset links: How many links were in the newsletter
• recipients: How many people received the email with the newsletter
• total opens: How many times the newsletter was opened
• unique opens: How many individuals opened the newsletter
• total clicks: The total number of clicks on the newsletter
• unique clicks: (unsure; provided by Tinyletter)
• notes: notes on the newsletter

Acknowledgements:

This dataset was collected by the uploader, Rachael Tatman. It is released here under a CC-BY-SA license.

Inspiration:

• Which format receives more views?
• Which format receives more clicks?
• Which receives more clicks/view?
• What’s the best time of day to send a tweet?