Article

ARSA: a sentiment-aware model for predicting sales performance using blogs

Authors:

Xiaohui YuAuthors Info & Claims

SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 607 - 614

https://doi.org/10.1145/1277741.1277845

Published: 23 July 2007 Publication History

Abstract

Due to its high popularity, Weblogs (or blogs in short) present a wealth of information that can be very helpful in assessing the general public's sentiments and opinions. In this paper, we study the problem of mining sentiment information from blogs and investigate ways to use such information for predicting product sales performance. Based on an analysis of the complex nature of sentiments, we propose Sentiment PLSA (S-PLSA), in which a blog entry is viewed as a document generated by a number of hidden sentiment factors. Training an S-PLSA model on the blog data enables us to obtain a succinct summary of the sentiment information embedded in the blogs. We then present ARSA, an autoregressive sentiment-aware model, to utilize the sentiment information captured by S-PLSA for predicting product sales performance. Extensive experiments were conducted on a movie data set. We compare ARSA with alternative models that do not take into account the sentiment information, as well as a model with a different feature selection method. Experiments confirm the effectiveness and superiority of the proposed approach.

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Cited By

Sokolová ZHarahus MJuhár JPleva MStaš JHládek D(2024)Comparison of Machine Learning Approaches for Sentiment Analysis in SlovakElectronics10.3390/electronics1304070313:4(703)Online publication date: 9-Feb-2024
https://doi.org/10.3390/electronics13040703
Wang JHuang JTu XWang JHuang ALaskar MBhuiyan A(2024)Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and ChallengesACM Computing Surveys10.1145/364847156:7(1-33)Online publication date: 14-Feb-2024
https://dl.acm.org/doi/10.1145/3648471
Menaga DLakshminarayanan A(2023)A Method for Predicting Movie Box-Office using Machine Learning2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)10.1109/ICESC57686.2023.10192928(1228-1232)Online publication date: 6-Jul-2023
https://doi.org/10.1109/ICESC57686.2023.10192928
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Index Terms

ARSA: a sentiment-aware model for predicting sales performance using blogs
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval

July 2007

946 pages

ISBN:9781595935977

DOI:10.1145/1277741

General Chairs:
Wessel Kraaij
TNO, The Netherlands
,
Arjen P. de Vries
CWI, The Netherlands
,
Program Chairs:
Charles L. A. Clarke
University of Waterloo, Canada
,
Norbert Fuhr
University of Duisburg-Essen, Germany
,
Noriko Kando
National Institute of Informatics, Japan

Copyright © 2007 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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Publication History

Published: 23 July 2007

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SIGIR07: The 30th Annual International SIGIR Conference

July 23 - 27, 2007

Amsterdam, The Netherlands

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Sokolová ZHarahus MJuhár JPleva MStaš JHládek D(2024)Comparison of Machine Learning Approaches for Sentiment Analysis in SlovakElectronics10.3390/electronics1304070313:4(703)Online publication date: 9-Feb-2024
https://doi.org/10.3390/electronics13040703
Wang JHuang JTu XWang JHuang ALaskar MBhuiyan A(2024)Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and ChallengesACM Computing Surveys10.1145/364847156:7(1-33)Online publication date: 14-Feb-2024
https://dl.acm.org/doi/10.1145/3648471
Menaga DLakshminarayanan A(2023)A Method for Predicting Movie Box-Office using Machine Learning2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)10.1109/ICESC57686.2023.10192928(1228-1232)Online publication date: 6-Jul-2023
https://doi.org/10.1109/ICESC57686.2023.10192928
Jassim MAbd DOmri M(2023)Machine learning-based new approach to films reviewSocial Network Analysis and Mining10.1007/s13278-023-01042-713:1Online publication date: 2-Mar-2023
https://doi.org/10.1007/s13278-023-01042-7
Vashishtha SGupta VMittal M(2023)Sentiment analysis using fuzzy logic: A comprehensive literature reviewWIREs Data Mining and Knowledge Discovery10.1002/widm.150913:5Online publication date: 20-Jun-2023
https://doi.org/10.1002/widm.1509
Zhao JXiong FJin P(2022)Enhancing Short-Term Sales Prediction with Microblogs: A Case Study of the Movie Box OfficeFuture Internet10.3390/fi1405014114:5(141)Online publication date: 4-May-2022
https://doi.org/10.3390/fi14050141
Wang YGuo JYuan CLi B(2022)Sentiment Analysis of Twitter DataApplied Sciences10.3390/app12221177512:22(11775)Online publication date: 19-Nov-2022
https://doi.org/10.3390/app122211775
Moghisi REl Morr CPace KHajiha MHuang J(2022)A Machine Learning Approach to Predict the Outcome of Urinary Calculi Treatment Using Shock Wave Lithotripsy: Model Development and Validation StudyInteractive Journal of Medical Research10.2196/3335711:1(e33357)Online publication date: 16-Mar-2022
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Huang ZIbragimov R(2022)Equity returns and sentimentDependence Modeling10.1515/demo-2022-010910:1(159-176)Online publication date: 14-Jun-2022
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Laskar MHoque EHuang J(2022)Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text SummarizationComputational Linguistics10.1162/coli_a_0043448:2(279-320)Online publication date: 9-Jun-2022
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