research-article

ADM-LDA: An aspect detection model based on topic modelling using the structure of review sentences

Authors:

Mohamad Saraee,

Franciska De JongAuthors Info & Claims

Journal of Information Science, Volume 40, Issue 5

Pages 621 - 636

https://doi.org/10.1177/0165551514538744

Published: 01 October 2014 Publication History

Abstract

Probabilistic topic models are statistical methods whose aim is to discover the latent structure in a large collection of documents. The intuition behind topic models is that, by generating documents by latent topics, the word distribution for each topic can be modelled and the prior distribution over the topic learned. In this paper we propose to apply this concept by modelling the topics of sentences for the aspect detection problem in review documents in order to improve sentiment analysis systems. Aspect detection in sentiment analysis helps customers effectively navigate into detailed information about their features of interest. The proposed approach assumes that the aspects of words in a sentence form a Markov chain. The novelty of the model is the extraction of multiword aspects from text data while relaxing the bag-of-words assumption. Experimental results show that the model is indeed able to perform the task significantly better when compared with standard topic models.

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Venugopalan MGupta D(2022)An enhanced guided LDA model augmented with BERT based semantic strength for aspect term extraction in sentiment analysisKnowledge-Based Systems10.1016/j.knosys.2022.108668246:COnline publication date: 21-Jun-2022
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cover image Journal of Information Science

Journal of Information Science Volume 40, Issue 5

October 2014

152 pages

ISSN:0165-5515

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Publisher

Sage Publications, Inc.

United States

Publication History

Published: 01 October 2014

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Cited By

Chang VXu QNg CGuizani MHossain M(2022)How Do People View COVID-19 VaccinesJournal of Global Information Management10.4018/JGIM.30081730:10(1-29)Online publication date: 29-Jul-2022
https://dl.acm.org/doi/10.4018/JGIM.300817
Venugopalan MGupta D(2022)An enhanced guided LDA model augmented with BERT based semantic strength for aspect term extraction in sentiment analysisKnowledge-Based Systems10.1016/j.knosys.2022.108668246:COnline publication date: 21-Jun-2022
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Guo YWang FXing CLu X(2022)Mining multi-brand characteristics from online reviews for competitive analysisElectronic Commerce Research and Applications10.1016/j.elerap.2022.10114153:COnline publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.elerap.2022.101141
Wang ZZheng YZhu HYang CChen T(2022)Transferable adversarial examples can efficiently fool topic modelsComputers and Security10.1016/j.cose.2022.102749118:COnline publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.cose.2022.102749
Peng XXu QGan W(2021)SBTMJournal of Information Science10.1177/016555152091712047:4(517-532)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1177/0165551520917120
Memon MLu YChen PMemon APathan MZardari Z(2021)An ensemble clustering approach for topic discovery using implicit text segmentationJournal of Information Science10.1177/016555152091159047:4(431-457)Online publication date: 1-Aug-2021
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Ekinci Eİlhan Omurca S(2020)Concept-LDAJournal of Information Science10.1177/016555151984585446:3(406-418)Online publication date: 18-Jun-2020
https://dl.acm.org/doi/10.1177/0165551519845854
Bagheri A(2020)Integrating word status for joint detection of sentiment and aspect in reviewsJournal of Information Science10.1177/016555151881145845:6(736-755)Online publication date: 18-Jun-2020
https://dl.acm.org/doi/10.1177/0165551518811458
Rana TCheah Y(2019)Sequential patterns rule-based approach for opinion target extraction from customer reviewsJournal of Information Science10.1177/016555151880819545:5(643-655)Online publication date: 1-Oct-2019
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Jelodar HWang YYuan CFeng XJiang XLi YZhao L(2019)Latent Dirichlet allocation (LDA) and topic modelingMultimedia Tools and Applications10.1007/s11042-018-6894-478:11(15169-15211)Online publication date: 1-Jun-2019
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