Sentiment Analysis

Data were collected through the Twitter API, focusing on specific vocabulary related to wildfires, hashtags commonly used during the Tubbs Fire, and terms and hashtags related to mental health, well-being, and physical symptoms associated with smoke and wildfire exposure. We focused exclusively on the period from October 8 to October 31, aligning precisely with the duration of the Tubbs Fire. The final dataset available for analysis consists of 90,759 tweets.

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18 Views

This data collection focuses on capturing user-generated content from the popular social network Reddit during the year 2023. This dataset comprises 29 user-friendly CSV files collected from Reddit, containing textual data associated with various emotions and related concepts.

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1354 Views

Supplementary material for article "A Group Decision-Making Method Based on the Experts’ Behaviour During the Debate". Two files containing the comments provided by four expert during a debate to select the best product.

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81 Views

Air travel is one of the most used ways of transit in our daily lives. So it's no wonder that more and more people are sharing their experiences with airlines and airports using web-based online surveys. This dataset aims to do topic modeling and sentiment analysis on Skytrax (airlinequality.com) and Tripadvisor (tripadvisor.com) postings where there is a lot of interest and engagement from people who have used it or want to use it for airlines.

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519 Views

Companion data of the paper "Using social media and personality traits to assess software developers’ emotions" submitted to the IEEE Access journal, 2022. This dataset contains the anonymized dataset used in the study, including the answers of demographic survey, the answers to the Big Five Inventory, the experiment protocol, the manual analysis from psychologists and participants, all generated charts and data analysis.

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344 Views

Twitter is one of the most popular social networks for sentiment analysis. This data set of tweets are related to the stock market. We collected 943,672 tweets between April 9 and July 16, 2020, using the S&P 500 tag (#SPX500), the references to the top 25 companies in the S&P 500 index, and the Bloomberg tag (#stocks). 1,300 out of the 943,672 tweets were manually annotated in positive, neutral, or negative classes. A second independent annotator reviewed the manually annotated tweets.

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8535 Views

India is known for its highly disciplined foreign policies, strategic location, vibrant and massive Diaspora. India envisages enhancing its scope of cooperation, trade and widens its sphere of relations with the Pacific. As a result, the world is witnessing the rise of Indo-Pacific ties. Before the 1980’s the keystone of the universe was called the Atlantic, but now a radical shift to the east is noticed by the term “Indo-Pacific‟.

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478 Views

This dataset was extracted from Twitter using keywords related to Dilma Roussef and Aécio Neves, that were the candidates of the second round of the 2014 presidential election in Brazil. This dataset contains texts in Portuguese and the respective classification of sentiments resulting from the techniques described in the article published in the 2018 IEEE International Conference on Data Mining Workshops - ICDMW (https://ieeexplore.ieee.org/abstract/document/8637504). 

 

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219 Views

This dataset includes 24,201,654 tweets related to the US Presidential Election on November 3, 2020, collected between July 1, 2020, and November 11, 2020. The related party name and sentiment scores of tweets, also the words that affect the score were added to the data set.

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6328 Views

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