Feature Based Sentiment Analysis of Product Reviews using Modified PMI-IR method
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2016 by IJCTT Journal|
|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.
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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Sentiment analysis, web crawler, semantic orientation, PMI-IR, summarization.