Descriptive statistics. by Maria Cyniak-Cieciura (15187990)

Mpox Narrative on Instagram: A Labeled Multilingual Dataset of Instagram Posts on Mpox for Sentiment, Hate Speech, and Anxiety Analysis by Nirmalya Thakur (18869896)

“…</li><li><b><i>Sentiment</i></b>: Results of sentiment analysis (using the preprocessed version of the translated Post Description) where each post was classified into one of the sentiment classes: fear, surprise, joy, sadness, anger, disgust, and neutral</li><li><b><i>Hate</i></b>: Results of hate speech detection (using the preprocessed version of the translated Post Description) where each post was classified as hate or not hate</li><li><b><i>Anxiety or Stress</i></b>: Results of anxiety or stress detection (using the preprocessed version of the translated Post Description) where each post was classified as stress/anxiety detected or no stress/anxiety detected.…”

A Labelled Dataset for Sentiment Analysis of videos on YouTube, TikTok, and other sources about the 2024 Outbreak of Measles by Nirmalya Thakur (18869896)

“…After developing this dataset, sentiment analysis (using VADER), subjectivity analysis (using TextBlob), and fine-grain sentiment analysis (using DistilRoBERTa-base) of the video titles and video descriptions were performed. This included classifying each video title and video description into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii) one of the subjectivity classes i.e. highly opinionated, neutral opinionated, or least opinionated, and (iii) one of the fine-grain sentiment classes i.e. fear, surprise, joy, sadness, anger, disgust, or neutral. …”