Aspect-based Pros and Cons Summarization from Social Media Sentiment Analysis

Abstract

More and more users use social media to share opinions on a variety of topics People’s opinions and sentiments expressed on social media are useful resources for consumers and companies that can help them to make decisions Due to information explosion it is difficult for people to read and summarize the sentiment orientation of social media Previous studies have often used a sentiment analysis method to mine and summarize user opinions of items at document level However extracting sentiment orientation at the document level has some limitations We cannot determine which aspects of the items that users are happy or unhappy about Sequential pattern mining can be implemented to extract such aspects However traditional sequential pattern mining has two problems for aspect extraction including lattice structure problem and flexibility problem Furthermore if we extract these aspects and classify their sentiment orientation precisely there will be too many aspects for people to summarize the information they want to see Because of the problems mentioned above this study proposed a framework of aspect-based pros and cons summarization with flexible sequential rule mining proposed to help users to summarize information and make decisions based on social media The aim of this study is to develop an aspect-based pros and cons summarization system Flexible sequential rule mining is proposed to discover sequential rules and utilize the rules to extract the aspects of an item Then sentiment orientation of aspects are classified via the machine-learning approach Upon sentiment classification of the aspects we use aspects and the sentiment information to summarize some pros and cons for a given query (item) based on social media In the experiment we built an experimental data set to evaluate the accuracy of aspect retrieval and aspect-based sentiment classification In the experiments on aspect retrieval the results showed that flexible sequential rule mining has high precision but low recall Furthermore it obtained better performance than baseline methods In the experiments on aspect-based sentiment classification the results showed that aspect retrieval is an important part of the sentiment classification of aspects Furthermore the performance of our model was better than that of baseline methods Finally we selected two products had the smallest and largest quantity of data to discuss the results of pros and cons summarization The performance of small data was better than that of large data Most of the experimental data belonged to a particular area Therefore the rules generated from the experimental dataset on this product may not be applicable to other contexts In this study we developed an aspect-based pros and cons summarization system Through building an experimental data set for evaluation and a case study we verified the availability and validity of our system Finally we expect the system will help people read and summarize sentiment orientation from social media that will assist them in making decisions or determine marketing strategies

Date of Award	2016 Aug 9
Original language	English
Supervisor	Jung-Hsien Chiang (Supervisor)