Identifying mentions about long-term experiences and sentiment change on a specific target based on linguistic analysis: application to a product review domain

Abstract

People post and share their experiences through social media on the web these days. The resulting usergenerated web documents have become a useful source of advice for making a decision or resolving difficulties because people can learn from others’ past successes or failures. Recently, in response to the rapid growth of such documents and great potential of experience-based information, researches have been conducted on analyzing experiences in user-generated web documents. Earlier work has addressed the issue on distinguishing “experience sentences” from others and has proposed a discrimination method based on the linguistic properties of the mentioned events in such sentences. However, such work has focused mostly on a single event at a sentence level in large-scale data, so that a meaningful series of a specific person’s experiences on a particular target has not been analyzed fully yet.

This dissertation presents a method to analyze mentions about target-oriented experiences. More specifically, we propose a novel method to identify mentions about a customer’s experiences on a particular product in two aspects: long-term experiences and sentiment change in such experiences. As for long-term experiences, the hypothesis is that the two linguistic expressions time expressions and product names fully capture the customer’s long-term experiences mentioned in a review. As for sentiment change, the hypothesis is that sentiment change can be determined by detecting the state in a such review such that the overall sentiment towards a product instance purchased at a certain time in the past may not be the same as the overall sentiment towards another instance purchased at the latest time.

In this dissertation, we address three major research questions. The first question is about identifying product names. Unlike previous researches on identification on a product entity level, instance level identification for instance distinction should be accounted for. Our rese...