short-paper

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

Authors:

Sébastien Fournier,

Patrice Bellot,

Leif Azzopardi,

Gabriella PasiAuthors Info & Claims

ICTIR '20: Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval

Pages 29 - 32

https://doi.org/10.1145/3409256.3409847

Published: 14 September 2020 Publication History

Abstract

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%.

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Cited By

Ullah IAlam SAli ZKhan MJabeen FKhusro S(2023)On the current state of query formulation for book searchArtificial Intelligence Review10.1007/s10462-023-10483-756:10(12085-12130)Online publication date: 19-Apr-2023
https://doi.org/10.1007/s10462-023-10483-7
Fujimoto TMurakami H(2022)A Book Recommendation System Considering Contents and Emotions of User Interests2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI)10.1109/IIAIAAI55812.2022.00039(154-157)Online publication date: Jul-2022
https://doi.org/10.1109/IIAIAAI55812.2022.00039
Seilsepour AAlizadeh MRavanmehr RBeheshti MNassiri R(2022)Self-supervised Sentiment Classification based on Semantic Similarity Measures and Contextual Embedding using metaheuristic optimizer2022 8th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)10.1109/ICSPIS56952.2022.10043914(1-7)Online publication date: 28-Dec-2022
https://doi.org/10.1109/ICSPIS56952.2022.10043914
Show More Cited By

Index Terms

Using Sentiment Analysis for Pseudo-Relevance Feedback in Social Book Search
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile devices
      1. E-book readers
2. Information systems

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Published In

cover image ACM Conferences

ICTIR '20: Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval

September 2020

207 pages

ISBN:9781450380676

DOI:10.1145/3409256

General Chairs:
Krisztian Balog
University of Stavanger, Norway
,
Vinay Setty
University of Stavanger, Norway
,
Program Chairs:
Christina Lioma
University of Copenhagen, Denmark
,
Yiqun Liu
Tsinghua University, China
,
Min Zhang
Tsinghua University, China
,
Klaus Berberich
HTW Saar & MPI for Informatics, Germany

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 September 2020

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EquipEx DILOH
UKRI's EPSRC

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ICTIR '20

Sponsor:

SIGIR

ICTIR '20: The 2020 ACM SIGIR International Conference on the Theory of Information Retrieval

September 14 - 17, 2020

Virtual Event, Norway

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Overall Acceptance Rate 235 of 527 submissions, 45%

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Cited By

Ullah IAlam SAli ZKhan MJabeen FKhusro S(2023)On the current state of query formulation for book searchArtificial Intelligence Review10.1007/s10462-023-10483-756:10(12085-12130)Online publication date: 19-Apr-2023
https://doi.org/10.1007/s10462-023-10483-7
Fujimoto TMurakami H(2022)A Book Recommendation System Considering Contents and Emotions of User Interests2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI)10.1109/IIAIAAI55812.2022.00039(154-157)Online publication date: Jul-2022
https://doi.org/10.1109/IIAIAAI55812.2022.00039
Seilsepour AAlizadeh MRavanmehr RBeheshti MNassiri R(2022)Self-supervised Sentiment Classification based on Semantic Similarity Measures and Contextual Embedding using metaheuristic optimizer2022 8th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)10.1109/ICSPIS56952.2022.10043914(1-7)Online publication date: 28-Dec-2022
https://doi.org/10.1109/ICSPIS56952.2022.10043914
Najar FBouguila N(2022)On smoothing and scaling language model for sentiment based information retrievalAdvances in Data Analysis and Classification10.1007/s11634-022-00522-617:3(725-744)Online publication date: 13-Oct-2022
https://doi.org/10.1007/s11634-022-00522-6
Ullah IKhusro S(2022)On the analysis and evaluation of information retrieval models for social book searchMultimedia Tools and Applications10.1007/s11042-022-13417-782:5(6431-6478)Online publication date: 27-Jul-2022
https://doi.org/10.1007/s11042-022-13417-7
Htait AAzzopardi LHasibi FFang YAizawa A(2021)Sentiment Intensity Prediction using Neural Word EmbeddingsProceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3471158.3472254(93-102)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3471158.3472254
Holur PShahsavari SEbrahimzadeh ETangherlini TRoychowdhury V(2021)Modelling social readers: novel tools for addressing reception from online book reviewsRoyal Society Open Science10.1098/rsos.2107978:12Online publication date: 22-Dec-2021
https://doi.org/10.1098/rsos.210797
Htait AAzzopardi L(2021)AWESSOME: An Unsupervised Sentiment Intensity Scoring Framework Using Neural Word EmbeddingsAdvances in Information Retrieval10.1007/978-3-030-72240-1_56(509-513)Online publication date: 30-Mar-2021
https://doi.org/10.1007/978-3-030-72240-1_56
Ullah IKhusro SAhmad I(2020)Improving social book search using structure semantics, bibliographic descriptions and social metadataMultimedia Tools and Applications10.1007/s11042-020-09811-8Online publication date: 3-Oct-2020
https://doi.org/10.1007/s11042-020-09811-8

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