Title

Technical Papers Parallel session-V: Opinion mining approaches on Amazon product reviews: A comparative study

Abstract/Description

The process of extracting of people's opinion, experience and emotions from reviews, blogs and other sources is known as opinion mining. This paper compares our lexicon dictionary based approach with n-grams with three famous Machine Leaning (ML) algorithms, which are random forest learner with word vector, decision tree learner with document vector, and random forest with n-gram. To predict positive and negative sentiments, Amazon's Product Review dataset has been used. Accuracy of each of these algorithms is calculated by using ROC curve in order to compare which algorithm performs best on a given Amazon dataset. Experimental result shows that lexicon based approach outperforms other machine learning techniques.

Location

Theatre 2, Aman Tower

Session Theme

Technical Papers Parallel session-V: Information Retrieval

Session Type

Parallel Technical Session

Session Chair

Dr. Khurram Junejo

Start Date

31-12-2017 2:40 PM

End Date

31-12-2017 3:00 PM

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Dec 31st, 2:40 PM Dec 31st, 3:00 PM

Technical Papers Parallel session-V: Opinion mining approaches on Amazon product reviews: A comparative study

Theatre 2, Aman Tower

The process of extracting of people's opinion, experience and emotions from reviews, blogs and other sources is known as opinion mining. This paper compares our lexicon dictionary based approach with n-grams with three famous Machine Leaning (ML) algorithms, which are random forest learner with word vector, decision tree learner with document vector, and random forest with n-gram. To predict positive and negative sentiments, Amazon's Product Review dataset has been used. Accuracy of each of these algorithms is calculated by using ROC curve in order to compare which algorithm performs best on a given Amazon dataset. Experimental result shows that lexicon based approach outperforms other machine learning techniques.