Predictive Analysis on Twitter: Techniques and Applications

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.

Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention

“…Additional measures are necessary to improve timely intervention [5]. Unobtrusive collection and analysis of social media data can provide a means for gathering insights about an individual's emotions, and suicidal ideation and behavior [33]. A system capable of gleaning digital markers of suicide risk assessment from social media conversations of a patient (see Figure 1) can help a mental health professional (MHP) for making informed decisions as the patients may be reluctant to directly share all the relevant information due to the social stigma associated with mental illness and suicide [22].…”

A Dynamic Bayesian Network Approach for Analysing Topic-Sentiment Evolution

“…Sentiment evolution is another sentiment analysis task that has attracted increasing attention from both academia and industry recently [6]. It is able to capture the sentiment dynamics of topics over time, and thus can provide a more in-depth analysis of textural data [6].…”

“…Sentiment evolution is another sentiment analysis task that has attracted increasing attention from both academia and industry recently [6]. It is able to capture the sentiment dynamics of topics over time, and thus can provide a more in-depth analysis of textural data [6]. Some approaches such as dynamic joint sentiment-topic model [33], neighbourhood based influence propagation model, and location-based dynamic sentiment-topic models [34] have been proposed to capture the evolution of the sentiment of topics.…”

“…information [4], and most existing sentiment analysis work focuses on this task [6]. For example, using natural language processing approaches such as Convolutional Neural Networks [7] to predict whether the sentiment polarity of a given tweet is ''Positive'' , ''Negative'' , or ''Neutral''.…”

“…On the other hand, topic level or hashtag level sentiment analysis that detects the overall or general sentiment tendency towards topics is also important in many scenarios [4], [6]. For example, people's overall sentiment tendency on the topic ''#Brexit'' on Twitter will be an important indicator of the outcome of political events.…”