DOLORES: a system for logic-based retrieval of multimedia objects
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
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Automatic Document Summarisation plays a central role in the process of providing the user with a quick access to information. Applications range from the generation of news headlines, to the aggregation of opinions extracted from reviews. Traditional topic-based summarisation systems are not always able to capture the sentiments expressed in a review. Major efforts in sentiment analysis have been put in the tasks of mining and classifying reviews according to their polarity. In this research, we investigate the use of summarisation techniques applied to reviews, and we propose a knowledge-based approach to summarisation, in the context of sentiment analysis. The proposed research is focused on three different aspects. Firstly, we investigate the application of summarisation techniques to sentiment classification. Capturing the key passage of a review can be beneficial for both a sentiment classifier, and for a user who could potentially understand the polarity of a review without reading the full text. Secondly, we investigate how to combine knowledge extracted from the reviews or integrated from external sources, with the purpose of producing opinion-oriented summaries. Thirdly, we analyse the possibility of generating personalised (user-oriented or query-biased) opinion-based summaries.