Query-relevant sentiment summarization based on facet identification and sentence clustering

Multi-document sentiment analysis is an important natural language processing problem. Summaries generated by these analyzers can greatly reduce the time necessary to read a collection of topically-related documents to locate the desired information needs of a user. With the ever-increasing globalization and technology of the modern day, analysis of online user reviews on different products is an especially pertinent application of the aforementioned problem. At present there are way too many user reviews on popular products for potential buyers to spend adequate time to read and extract the most salient product details and opinions of previous buyers. In solving this problem, we propose a fully-automated summarizer to reduce the workload of online customers. The proposed system takes a user query and extracts the most relevant and essential comments made by individual reviewers. As opposed to existing multi-document summarization approaches, our summarizer compiles comprehensive reviews by extracting important facets and sentiment information based on various sentence features rather than applying complex machine learning algorithms. The design of our summarizer is easy to understand and implement, without the required massive training data and excessive training time. The conducted empirical study shows that the proposed summarization system outperforms current state-of-the-art multi-document sentiment summarization approaches.