QMOS: Query-based multi-documents opinion-oriented summarization - Abdi, Asad; Shamsuddin, Siti Mariyam; Aliguliyev, Ramiz M. (Information processing & management, 2018)
•It combines multiple sentiment dictionaries to improve word coverage limit.•It integrates negation, but-clause, sarcasm, subjective/objective, question/ conditional handling.•It integrates the semantic relations between words, and their syntactic composition to capture the meaning in comparison between a sentence and the user query.•It expands the words in the query and sentences to tackle the problem of information limit.•Experiment results on Blog06 & DUC2006 displayed that it is to be preferred over the existing methods. Sentiment analysis concerns the study of opinions expressed in a text. This paper presents the QMOS method, which employs a combination of sentiment analysis and summarization approaches. It is a lexicon-based method to query-based multi-documents summarization of opinion expressed in reviews. QMOS combines multiple sentiment dictionaries to improve word coverage limit of the individual lexicon. A major problem for a dictionary-based approach is the semantic gap between the prior polarity of a word presented by a lexicon and the word polarity in a specific context. This is due to the fact that, the polarity of a word depends on the context in which it is being used. Furthermore, the type of a sentence can also affect the performance of a sentiment analysis approach. Therefore, to tackle the aforementioned challenges, QMOS integrates multiple strategies to adjust word prior sentiment orientation while also considers the type of sentence. QMOS also employs the Semantic Sentiment Approach to determine the sentiment score of a word if it is not included in a sentiment lexicon. On the other hand, the most of the existing methods fail to distinguish the meaning of a review sentence and user's query when both of them share the similar bag-of-words; hence there is often a conflict between the extracted opinionated sentences and users’ needs. However, the summarization phase of QMOS is able to avoid extracting a review sentence whose similarity with the user's query is high but whose meaning is different. The method also employs the greedy algorithm and query expansion approach to reduce redundancy and bridge the lexical gaps for similar contexts that are expressed using different wording, respectively. Our experiment shows that the QMOS method can significantly improve the performance and make QMOS comparable to other existing methods.