A Hybrid Approach to Sentiment Analysis of Technical Article Reviews
Автор: Babaljeet Kaur, Naveen Kumari
Журнал: International Journal of Education and Management Engineering(IJEME) @ijeme
Статья в выпуске: 6 vol.6, 2016 года.
Бесплатный доступ
Sentiment analysis is similar to opinion mining, which is a popular research problem to search out in the field of NLP. Sentiment analysis determines the perspective of the author and identifies the positive, negative and neutral reviews. It provides the reviews or opinions of people's on text, article and product which can be positive, negative or neutral. Reviews on the different websites, social networking sites is an important source to collect the information regarding various brands of product and new features in technology (e.g. Windows, Mobiles). During the sentiment analysis various classification tools within the NLP are used to find out the positivity and negativity of reviews or comments. The paper presents a length aware hybrid approach to analyses the reviews either as positive or negative and present approach is tested on SuperFetch data set. The present approach is a combination of both supervised machine learning techniques that are Support Vector Machine and K-Nearest Neighbor in which SVM is working great for large size review and KNN is working best for small size review.
Sentiment analysis, SVM, KNN, SuperFetch review
Короткий адрес: https://sciup.org/15014036
IDR: 15014036
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