Feature Based Sentiment Analysis of Product Reviews using Modified PMI-IR method
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International Journal of Computer Trends and Technology (IJCTT) | 
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| © 2016 by IJCTT Journal | ||
| Volume-34 Number-2 |
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| Year of Publication : 2016 | ||
| Authors : Sanjay Kalamdhad, Shivendra Dubey, Mukesh Dixit | ||
| DOI : 10.14445/22312803/IJCTT-V34P120 |
Sanjay Kalamdhad, Shivendra Dubey, Mukesh Dixit "Feature Based Sentiment Analysis of Product Reviews using Modified PMI-IR method". International Journal of Computer Trends and Technology (IJCTT) V34(2):115-121, April 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
In online product reviews users discuss
about products and its features. A product may have
hundreds or thousands of reviews, consumers share
their experience about products and comments about
products characteristics. These product reviews may
have positive or negative sentiments. A positive
sentiment contains good opinion about product and
its features similarly a negative sentiment tells
drawbacks and problems of product and its features.
Feature may be part of the product or its
characteristics. In this paper we use modified PMIIR
method for analyzing the sentiments in online
product reviews about the various features of
products. We download the product reviews from
internet using the web crawler and stored it in
inverted index format. Using the parts-of speech
tagging, extract the two-word opinion phrases and
calculates the semantic orientation by measuring the
mutual information between each phrases and
positivity and negativity. Summary of sentiments of
each feature is presented based on average semantic
orientation value. Summarization shows the
sentiment classification of features of products.
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Keywords
Sentiment analysis, web crawler,
semantic orientation, PMI-IR, summarization.