alexa Real Time Sentiment Classification Using Unsupervised
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
Open Access

OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Special Issue Article

Real Time Sentiment Classification Using Unsupervised Reviews

M.E, Department of CSE, Sri Krishna College of Engineering and Technology, Coimbatore, India
Related article at Pubmed, Scholar Google


Sentiment classification is an important task in everyday life. Users express their opinion about their product, movies and so on. All the web page contains reviews that are given by users expressing different polarity i.e. positive or negative. It is useful for both the producer and consumer to know what people think about the particular product or services based on their reviews. Automatic document classification is the task of classifying the reviews based on the sentiment expressed by the reviews. Sentiment is expressed differently in different domains. The data trained on one domain cannot be applied to the data trained on another domain. The cross domain sentiment classification overcomes these problems by creating thesaurus for labeled data on the target domain and unlabeled data from source and target domains. Sentiment sensitivity is achieved by creating thesaurus. The created thesaurus is used to expand the feature vector. Amazon reviews are taken from different products and the thesaurus is created for multiple domains which contain both positive and negative words. Thus the created sentiment sensitive thesaurus captures the words with similar sentiment. The proposed method the reviews are analyzed by unsupervised method and sentiment can be analyzed for each sentence.


Share This Page

Additional Info

Loading Please wait..
Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version