New Avenues in opinion mining: Considering Dual Sentiment Analysis
Pankaj Ramchandra Chandre, Rohan Raj Himanshu Raj · International Journal of Scientific Research in Education · 2017
In the modern world, due to advancement and outreach of technology, ease of access to any kind of product or service is growing immensely. Subjective attitude (i.e. Sentiment) ranging from that of products, news, movies to that of social networking mediums is being given for each and every product now-a-days. The market now, not only values the expert opinion but the reviews of masses have taken an equal importance as they are the one using the products and services. For the betterment of products, the input must be understood and the data must be analyzed by proper Machine Learning techniques along with Natural Language Processing in order to draw the conclusions and comprehending the overall situation. The topic-based text classification based on the Bag-of-Words model has some fundamental inadequacies, although various algorithms and classifiers (like naive Bayes, support vector machines) are already analyzing sentiments and giving categorical feedback as a generic output. Polarity shift problem restricts the performance of these existing models. To address this problem for sentiment classification, Dual sentiment analysis (DSA) has been expanded from a 2 facet classification to a 3 facet classification which considers neutral reviews from the dataset as well for better accuracy and understanding. For each training and test review, a novel data expansion technique is being proposed that will use opposite class labels of positive and negative sentiments in one to one correspondence for a dual training and dual prediction algorithm. A corpus method based pseudo-antonym dictionary has also been proposed to remove the single language (English) based restriction and to maintain domain consistency as it will be pairing up words on the basis of sentiment strength. Keywords- Natural Language Processing, Bag-of-Words, Machine Learning, Dual Sentiment Analysis, Opinion mining, Naive Bayes, Support Vector Machines, Dataset, Polarity shift, Corpus Method