Predictive Analysis on Twitter: Techniques and Applications

TL;DR

This paper reviews techniques and applications of predictive analysis on Twitter data, highlighting methods for sentiment, emotion, and domain knowledge analysis across various fields, and discussing successful case studies.

Contribution

It provides a comprehensive overview of state-of-the-art predictive analysis techniques and their applications on Twitter data, including fine-grained sentiment and emotion analysis.

Findings

Effective techniques for sentiment and emotion analysis on Twitter.

Successful applications in healthcare, politics, and social sciences.

Insights into decision-making based on Twitter data analysis.

Abstract

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.