IR @ Goa University

Improving coherence of topic based aspect clusters using domain knowledge

Show simple item record

dc.contributor.author Asnani, K.S.
dc.contributor.author Pawar, J.D.
dc.date.accessioned 2019-01-07T04:55:45Z
dc.date.available 2019-01-07T04:55:45Z
dc.date.issued 2018
dc.identifier.citation Computacion y Sistemas. 22(4); 2018; 1403-1414. en_US
dc.identifier.uri http://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/2401
dc.identifier.uri http://irgu.unigoa.ac.in/drs/handle/unigoa/5557
dc.description.abstract Web is loaded with opinion data belonging to multiple domains. Probabilistic topic models such as Probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) have been popularly used to obtain thematic representations called topic-based aspects from the opinion data. These topic-based aspects are then clustered to obtain semantically related groups, by algorithms such as Automated Knowledge LDA (AKL). However, there are two main shortcomings with these algorithms namely the cluster of topics obtained sometimes lack coherence to accurately represent relevant aspects in the cluster and the popular or common words which are referred to as the generic topics are found to occur across clusters in different domains. In this paper we have used context domain knowledge from a publicly available lexical resource to increase the coherence of topic-based aspect clusters and discriminate domain-specific semantically relevant topical aspects from generic aspects shared across the domains. BabelNet was used as the lexical resource. The dataset comprised of product reviews from 36 product domains, containing 1000 reviews from each domain and 14 clusters per domain. Also, frequent topical aspects across topic clusters indicate occurrence of generic aspects. The average elimination of incoherent aspects was found to be 28.84%. The trend generated by UMass metric shows improved topic coherence and also better cluster quality is obtained as the average entropy without eliminated values was 0.876 and with elimination was 0.906. en_US
dc.publisher Centro de Investigacion en Computacion (CIC) del Instituto Politecnico Nacional (IPN) en_US
dc.subject Computer Science and Technology en_US
dc.title Improving coherence of topic based aspect clusters using domain knowledge en_US
dc.type Journal article en_US
dc.identifier.impf cs


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search IR


Advanced Search

Browse

My Account