Review on recent advances in information mining from big consumer opinion data for product design

In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design

Question-based Text Summarization

In the modern information age, finding the right information at the right time is an art (and a science). However, the abundance of information makes it difficult for people to digest it and make informed choices. In this thesis, we aim to help people who want to quickly capture the main idea of a piece of information before they read the details through text summarization. In contrast with existing works, which mainly utilize declarative sentences to summarize a text document, we aim to use a few questions as a summary. In this way, people would know what questions a given text document can address and thus they may further read it if they have similar questions in mind. A question-based summary needs to satisfy three goals, relevancy, answerability, and diversity. Relevancy measures whether a few questions can cover the main points that discussed in a text document; answerability measures whether answers to the questions are included in the text document; and diversity measures whether there is redundant information carried by the questions. To achieve the three goals, we design a two-stage approach which consists of question selection and question diversification. The question selection component aims to find a set of candidate questions that are relevant to a text document, which in turn can be treated as answers to the questions. Specifically, we explore two lines of approaches that have been developed for traditional text summarization tasks, extractive approaches and abstractive approaches to achieve the goals of relevancy and answerability, respectively. The question diversification component is designed to re-rank the questions with the goal of rewarding diversity in the final question-based summary. Evaluation on product review summarization tasks for two product categories shows that the proposed approach is effective for discovering meaningful questions that are representative for individual reviews. This thesis opens up a new direction in the intersection of information retrieval and natural language processing. Despite the evaluation on the product review domain, the thesis provides a general solution for question selection for many interesting applications and discusses the possibility of extending the problem to other domain-specific question-based text summarization tasks.Ph.D., Information Science -- Drexel University, 201

Product review summarization from a deeper perspective

10.1145/1998076.1998134Proceedings of the ACM/IEEE Joint Conference on Digital Libraries311-31