General unsupervised explanatory opinion mining from text data

Due to the abundance and rapid growth of opinionated data on the Web, research on opinion mining and summarization techniques has received a lot of attention from industry and academia. Most previous studies on opinion summarization have focused on predicting sentiments of entities and aspect-based rating for the entities. Although existing techniques can provide general overview of opinions, they do not provide detailed explanation of the underlying reasons of the opinions. Therefore, people still need to read through the classified opinionated comments to find out why people expressed those opinions.;To overcome this challenge, we propose a series of works in general unsupervised explanatory opinion mining from text data. We propose three new problems for further summarizing and understanding explanatory opinions and general unsupervised solutions for each problem. First, we propose (1) Explanatory Opinion Summarization (EOS) summarizing opinions that can explain a particular polarity of sentiment. EOS aims to extract explanatory text segments from input opinionated texts to help users better understand the detailed reasons of the sentiment. We propose several general methods to measure explanatoriness of text and identify explanatory text segment boundary. Second, we propose (2) Contrastive Opinion Summarization (COS) summarizing opinions that can explain mixed polarities. COS extracts representative and contrastive opinions from opposing opinions. By automatically pairing and ranking comparative opinions, COS can provide better understanding of contrastive aspects from mixed opinions. Third, we consider temporal factor of text analysis and propose (3) Causal Topic Mining summarizing opinions that can explain an external time series data. We first propose a new information retrieval problem using time series as a query whose goal is to find relevant documents in a text collection of the same time period, which contain topics that are correlated with the query time series. Second, beyond causal documents retrieval, we propose Iterative Topic Modeling with Time Series Feedback (ITMTF) framework that mines causal topics by jointly analyzing text and external time-series data. ITMTF naturally combines any given probabilistic topic model with causal analysis techniques for time series data such as Granger Test to discover topics that are both coherent semantically and correlated with time series data.;Proposed techniques have been shown to be effective and general enough to be applied for potentially many interesting applications in multiple domains, such as business intelligence and political science, with minimum human supervision.