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Sentiment Analysis and Similarity Evaluation for Heterogeneous-Domain Product Reviews

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Mangal Singh, Tabrez Nafis, Neel Mani

Mangal Singh, Tabrez Nafis and Neel Mani. Sentiment Analysis and Similarity Evaluation for Heterogeneous-Domain Product Reviews. International Journal of Computer Applications 144(2):16-19, June 2016. BibTeX

	author = {Mangal Singh and Tabrez Nafis and Neel Mani},
	title = {Sentiment Analysis and Similarity Evaluation for Heterogeneous-Domain Product Reviews},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2016},
	volume = {144},
	number = {2},
	month = {Jun},
	year = {2016},
	issn = {0975-8887},
	pages = {16-19},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2016910112},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Sentiment analysis and classification is a prominent research topic in academics as well as in industrial field. Since each customer reviews text always state emotion about a target domain, sentiment classification is a highly domain dependent task and present study considered the reviews from heterogeneous domains. Generally researchers classify the customer review with positive, negative and neutral sentiments but a positive review can be highly positive and a negative review can be highly negative, so sentiment analysis about a review can be more effective if a sentiment scale is also defined for such greater degree of positivity or negativity. We defined a framework to classify heterogeneous product reviews with degree of polarity on a sentiment scale of range -2 to 2. For each review, an intermediate form is calculated using sentiment vectors which is further processed to calculate the sentiment polarity magnitude and similarity of reviews.


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Sentiment Vector, Intermediate Form, Sentiment Polarity Magnitude.