An Efficient Aspect-Based Opinion Mining on Smart Phone Reviews With LDA
Keywords:
Sentiment Analysis; LDA; iPhone X; E-Commerce, Opinion Mining, Information Systems, Information Retrieval, Retrieval Tasks and Goals, Sentiment Analysis.Abstract
With the development of e-commerce stage, online shopping has become an easy and preferable mode of shopping. As one of the largest e-commerce stages worldwide, Amazon enjoy numerous user communities. Volumes of user-generated information of users' preferences and opinions towards items, for the most part for specific aspects of a ware, popped up every day. Albeit loaded with information, these texts are often unstructured information that requires a careful analysis for the two consumers and manufactures to extract meaningful and relevant information. Customary lexicon-based sentiment analysis considers extremity score of words however ignores the differences among aspects. Document level subject modeling help overcome these lacunae. Right now, guarantee that the aspects ought to likewise be weighted for featuring significance of different aspects appropriate to a space. Along these lines, manufacturers can understand what potential consumers may need as improvement in the expected items. To showcase our framework, more than 400,000 Amazon unlocked phone reviews were collected as preparing information. LDA models were used to cluster subject words with their corresponding likelihood values. Based on the machine learning framework results, a corpus of nearly 1,000 Amazon reviews of a new mobile phone mode, iPhone X, was tested utilizing this framework to perform subject labeling and sentiment analysis. Performance analysis was done utilizing Confuse Matrix and F-measure.
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