Supervised Sentiment Classification using DTDP algorithmJournal: International Journal for Scientific Research and Development | IJSRD (Vol.3, No. 10)
Publication Date: 2016-01-01
Authors : S.Revathi; B. Nagarajan;
Page : 645-648
Keywords : Natural Language Processing; Sentiment Analysis; Classification; Prediction;
Sentiment analysis is the process widely used in all fields and it uses the statistical machine learning approach for text modeling. The primarily used approach is Bag-of-words (BOW). Though, this technique has some limitations in polarity shift problem. Thus, here we propose a new method called Dual sentiment analysis (DSA) which resolves the polarity shift problem. Proposed method involves two approaches such as dual training and dual prediction (DPDT). First, we propose a data expansion technique by creating a reversed review for training data. Second, dual training and dual prediction algorithm is developed for doing analysis on sentiment data. The dual training algorithm is used for learning a sentiment classifier and the dual prediction algorithm is developed for classifying the review by considering two sides of one review.
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Last modified: 2016-01-08 18:08:00