|Title||Predictive Analysis on Twitter: Techniques and Applications|
|Publication Type||Book Chapter|
|Year of Publication||2018|
|Authors||Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, Ismailcem Budak Arpinar|
|Book Title||Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining|
|Keywords||Citizen sensing, Community evolution, Demographic prediction, Drug trends, Election prediction, event analysis, Harassment detection, machine learning, Mental Health, Semantic Social Computing, Sentiment-Emotion-Intent Analysis, social media analysis, Spatio-temporalthematic analysis, Stock Market prediction|
Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories.
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