Opinion Mining Using Decision Tree Based Feature Selection through Manhattan Hierarchical Cluster Measure

Opinion mining plays a major role in text mining applications in consumer attitude detection, brand and product positioning, customer relationship management, and market research. These applications led to a new generation of companies and products meant for online market perception, reputation management and online content monitoring. Subjectivity and sentiment analysis focus on private states automatic identification like beliefs, opinions, sentiments, evaluations, emotions and natural language speculations. Subjectivity classification labels data as either subjective or objective, whereas sentiment classification adds additional granularity through further classification of subjective data as positive/negative or neutral. Features are extracted from the data for classifying the sentiment. Feature selection has gained importance due to its contribution to save classification cost with regard to time and computation load. In this paper, the main focus is on feature selection for Opinion mining using decision tree based feature selection. The proposed method is evaluated using IMDb data set, and is compared with Principal Component Analysis (PCA). The experimental results show that the proposed feature selection method is promising.