Using Sentiment Analysis for Pseudo-Relevance Feedback in Social Book Search

Amal Htait, Sebastien Fournier, Patrice Bellot, Leif Azzopardi, Gabriella Pasi

Research output: Chapter in Book/Published conference outputConference publication


Book search is a challenging task due to discrepancies between the content and description of books, on one side, and the ways in which people query for books, on the other. However, online reviewers provide an opinionated description of the book, with alternative features that describe the emotional and experiential aspects of the book. Therefore, locating emotional sentences within reviews, could provide a rich alternative source of evidence to help improve book recommendations. Specifically, sentiment analysis (SA) could be employed to identify salient emotional terms, which could then be used for query expansion? This paper explores the employment ofSA based query expansion, in the book search domain. We introduce a sentiment-oriented method for the selection of sentences from the reviews of top rated book. From these sentences, we extract the terms to be employed in the query formulation. The sentence selection process is based on a semi-supervised SA method, which makes use of adapted word embeddings and lexicon seed-words.Using the CLEF 2016 Social Book Search (SBS) Suggestion TrackCollection, an exploratory comparison between standard pseudo-relevance feedback and the proposed sentiment-based approach is performed. The experiments show that the proposed approach obtains 24%-57% improvement over the baselines, whilst the classic technique actually degrades the performance by 14%-51%.
Original languageEnglish
Title of host publicationICTIR '20 - Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval
Publication statusPublished - 14 Sept 2020


  • Query Expansion
  • Sentiment Analysis
  • Pseudo-Relevance Feedback


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