ANNOTATING FEATURES EXTRACTED THROUGH LATENT DIRICHLET ALLOCATION FOR FEATURE BASED OPINION MINING

Padmapani Prakash Tribhuvan, Sunil G. Bhirud, Ratnadeep R. Deshmukh

Abstract


Online product reviews contains opinions about products and their features. These product reviews are plain text and therefore analysis of these reviews requires more efforts. In this paper, we tackle the problem of features based opinion mining of product reviews using LDA topic model and proposed annotation algorithm. We proposed an architecture for feature based opinion mining based on topic models and an algorithm that automatically annotates features extracted through LDA topic model. The experimental result shows that the algorithm gives average feature annotation accuracy 77.14%, average positive polarity annotation accuracy 86.02% and average negative polarity annotation accuracy 88.57%. The algorithm can be used with different topics models as well.

Keywords


Feature-Based Opinion Mining; Review Summarization;Topic Annotation;Aspect-Based Sentiment Analysis;Topic Models;Latent Dirichlet Allocation

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DOI: https://doi.org/10.26483/ijarcs.v8i9.4939

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