2019/06/15 by Tunazzina Islam, Islam, Tunazzina · 2 citations
Computer Science · Psychology · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Computers and Society (cs.CY) #Eating Disorders and Behaviors #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.AI #cs.CL #cs.CY #cs.LG
paper · pdf · doi:10.48550/arxiv.1906.07668
In Proceedings of 8th KDD Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM) @KDD 2019. arXiv admin note: substantial text overlap with arXiv:1906.02132
arxiv created 2019/06/15 · openalex publication_date 2019/06/15 · arxiv updated 2020/12/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Nowadays social media is a huge platform of data. People usually share their interest, thoughts via discussions, tweets, status. It is not possible to go through all the data manually. We need to mine the data to explore hidden patterns or unknown correlations, find out the dominant topic in data and understand people's interest through the discussions. In this work, we explore Twitter data related to health. We extract the popular topics under different categories (e.g. diet, exercise) discussed in Twitter via topic modeling, observe model behavior on new tweets, discover interesting correlation (i.e. Yoga-Veganism). We evaluate accuracy by comparing with ground truth using manual annotation both for train and test data.