Product Features Extraction for Feature Based Opinion Mining using Latent Dirichlet Allocation
DOI:
https://doi.org/10.26438/ijcse/v5i10.128131Keywords:
Feature-Based Opinion Mining, Aspect-Based Sentiment Analysis, Topic Models, Latent Dirichlet AllocationAbstract
Unstructured product reviews are difficult to analyse. By applying feature-based opinion mining on product reviews, we can analyse product reviews. In Feature Based Opinion Mining, method of extracting features plays very important role. Performance of feature based opinion mining is depends on how features are extracted from product reviews. In this paper, we discussed how Latent Dirichlet Allocation topic model can be used for product features extraction. We discussed a methodology to extract product features using Latent Dirichlet Allocation topic model. We applied basic Latent Dirichlet Allocation (LDA) topic model on 24259 product reviews of 7 product categories to extract product features. We inferred the model using Gibbs Sampler. The result shows that LDA model extracts product reviews efficiently.
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