A query-based multi-document sentiment summarizer

Review websites, such as Epinions.com, which offer users a platform to share their opinions on diverse products and services, provide a valuable source of opinion-rich information. Browsing through archived reviews to locate different opinions on a product or service, however, is a time-consuming and tedious task, and in most cases, the large amount of available information is difficult for users to absorb. To facilitate the process of synthesizing opinions expressed in reviews on a product or service P specified in a user query/question Q, we introduce QMSS, a query-based multi-document sentiment summarizer. QMSS creates a summary for Q, which either reflects the general opinions on P or is tailored to specific facets (i.e., features) and/or sentiment of P as specified in Q. QMSS (i) identifies the facets addressed in reviews retrieved for Q, (ii) employs a sentence-based, sentiment classifier to determine the polarity of each sentence in each review, and (iii) clusters sentences in reviews according to the facets captured in the sentences, which are identified using a keyword-label extraction algorithm. This process dictates which sentences in the reviews should be included in the summary for Q. Empirical studies have verified that QMSS is highly effective in generating summaries that satisfy users' information needs and ranks on top among the state-of-the-art query-based multi-document sentiment summarizers.