With very large volume of data, useful aspects are often conveyed through latent semantics. We deal with extraction of relevant aspects automatically. Lack of training data: Training may be necessary in supervised aspect discovery but practically there is lack of already existing pre-annotated data.įundamentally, the task of aspect based analysis comprises of identifying and extracting aspects of an entity in a domain on which opinions have been expressed. Noisy: The chat style of social communication involves use of slang in the form of misspellings and ungrammatical words. Varied length messages: Social media text comprises of short and long length messages where the related semantic is highly dispersed in the content. Keywords: Topic-based aspect extraction aspect filtering aspect coherence lexical resource BabelNet context domain knowledge 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. The average elimination of incoherent aspects was found to be 28.84%. Also, frequent topical aspects across topic clusters indicate occurrence of generic aspects. The dataset comprised of product reviews from 36 product domains, containing 1000 reviews from each domain and 14 clusters per domain. BabelNet was used as the lexical resource. 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. 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. These topic-based aspects are then clustered to obtain semantically related groups, by algorithms such as Automated Knowledge LDA (AKL). 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. Web is loaded with opinion data belonging to multiple domains.
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