ENHANCED SENTIMENTAL ANALYSIS THROUGH DECISION SUPPORT SYSTEM BASED ON LEARNING TECHNIQUES USING NLTK
Keywords:
NLP, Machine Learning, SVM, Sentimental AnalysisAbstract
In recent years, technological improvements in computing have led to the development of sophisticated decision support systems to provide support to the customers who are using social networks for getting services. In the past, certain researchers grouped product and hotel reviews into positive and negative slots, which were used to make decisions to select suitable hotels, products and services for customers and to provide guidelines to the business personalities involved in hotels. Today, people form online groups and openly discuss not only the pros - of, for instance, hotels - but also air complaints. If negative feedback is not addressed properly by hotel service providers, it will likely increase and the hotel’s popularity downsized. Food served to customers depends on the preparation as well as the cost, location and times at which it is served. Further, the attitude of the sales people and hotel staff, in general, plays a key role in customers’ decisions. Thus, online customer feedback through social media is useful for customer behavior analysis, crucial for the success of business. A recommendation system which addresses all these issues can provide customers better options in their choice of hotels and services.
In this proposal, two new classification algorithms are proposed. One is based on a new type of support vector machines called group support vector machines to perform major, and sub classification, of sentiments, as well as form groups based on people’s sentiments with respect to changes in times and locations. The intelligent group support vector machine algorithm proposed in this thesis improves classification accuracy to provide accurate recommendations. The main advantage of the proposed work is that it helps identify people with similar interests, based on sentiments identified from tweets, and form interested groups for animated discussions on absorbing topics.
A new clustering algorithm is proposed in this research work which is useful in forming groups based on clusters. In this work, a new genetic weighted K-means clustering algorithm is proposed to detect proper cluster structures from two datasets, Twitter and Facebook. The genetic algorithm chosen here to perform clustering is an effective technique that improves classification accuracy.
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