Structured sentiment analysis in social media

Abstract

In recent years, social media platforms, such as Twitter and Webio, have become popular sources of information on the web. These platforms contain a wealth of valuable information about user opinions, user interests, events and more. People typically use these platforms to discuss different topics, share their opinions about them and engage in question-andanswer sessions. For example, regarding smartphones, users might discuss the main aspects of a smartphone, such as the overall design, battery capacity, screen size and camera. The natural hierarchical structure of those concepts is often hidden in social media. Discovering the hidden structure can helps users understand people' preference to a certain topic at different levels of granularity, and show the reasons why they prefer this topic. Over the past decade, research on hierarchical topic models has shown considerable progress. However, these studies may not always be directly applicable to social media due to the shortness and the shallow meaning of social media messages.There are three major challenges when dealing with social media texts. Firstly, compared with traditionally long texts, social media texts suffer from sparsity, and this issue may result in an incomprehensible and incorrect concept hierarchy. Secondly, social media contains useful information such as social opinions and information about users. Most existing methods perform a flat sentiment analysis on each extracted aspects independently, and ignore the concept hierarchy. In fact, we need to make the sentiment analysis finegrained in order to simultaneously extract the aspects and summarise people' opinions on those discovered aspects. Thirdly, the current models only discover the concept hierarchy ignoring the community structure of users. Maintaining the consistency of user's interest on several communities according to various topics and sentiment information is a challenging problem.In this thesis, the limitations of the existing work are addressed and effective solutions are proposed. First, in order to discover the hierarchical structure of social media content, a novel approach called the context coherence model (CCM) is proposed. It recursively top ii down: (1) organizes the concepts discussed by users in social media texts; and (2) identifies the hierarchical relations among concepts. In the CCM, a new measurement called context coherence is introduced that analyses words in social media texts and determines the similarities among them. Then, the hierarchical relationship between words is determined by recursively partitioning the whole corpus into smaller parts according to the similarity results. Finally, a merging operation is performed to find similar words, group them under the same topic and remove duplicated topics. The approach is evaluated on two real-world data sets. The experiments show that the proposed approach can effectively reveal the hidden structure in social media.Opinions are now reflected in social media on a wide range of topics: trends in pop music, fashion...