Topic-Based Affective Social Media Mining
Date Issued
2015
Date
2015
Author(s)
Yang, Fu-Chen
Abstract
Online social networks expedite social interactions where people create, share or exchange information and ideas. The contents generated by users in a social network usually contain a large volume of user opinions and feelings. They can be used as an effective vehicle to analyze product preferences, business strategies, marketing campaigns, social events, political movements, and healthcare experiences. Therefore, in this dissertation, we propose three LDA-based methods, called MPM (Mining Perceptual Maps), MAE (Mining Arousing Events) and MSS (Mining Social Support), to mine affective social media in social networks. The MPM method automatically builds perceptual maps and radar charts from consumer reviews based on users’ sentiment polarities toward different product features. Mining perceptual maps and radar charts can help companies gain knowledge of their and competitors’ products. The MAE method extracts emotionally arousing events from a collection of news documents based on readers’ emotions and intensities, where every news document contains a news article and some readers’ comments. Mining emotionally arousing events may provide a quick reference for politicians and a new aspect for hot news recommendation for readers. The MSS method mines social support and users’ emotion transitions from online healthcare social media. Mining social support by considering emotion transitions may help us better understand patients’ needs, attitudes and opinions, and provide more appropriate assistance since the changes of emotions often coincide with the changes of attitudes. Experimental results show that the MPM method can find the strengths and weaknesses of various mobile phones of different brands and different levels of prices from consumer reviews. The MAE method can better predict the readers’ emotions and intensities for unseen news articles, and discover better-quality and more subtle events using intensity. The MSS method shows that people with different diseases may express very different negative emotion transitions, and need various types of social support.
Subjects
Data Mining
LDA
Sentiment Analysis
Emotion Transition
Social Support
Consumer Reviews
News Documents
Healthcare
Type
thesis
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