eSAP-A-decision-support-framework-for-enhanced-sentiment-analysis-and-polarity-classification
US$52.57
10000 in stock
SupportDescription
Sentiment analysis or opinion mining is an imperative research area of natural language processing. It is used to determine the writer’s attitude or speaker’s opinion towards a particular person, product or topic. Polarity or subjectivity classification is the process of categorizing a piece of text into positive or negative classes. In recent years, various super-vised and unsupervised methods have been presented to accomplish sentiment polarity detection. SentiWordNet (SWN) has been extensively usedas a lexical resource for opinion mining. This research incorporates SWN as the labeled training corpus where the senti-mentscores are extracted based on the part of speech information. A vocabulary SWN-V withrevised sentiment scores, generated from SWN, is then used for Support Vector Machines model learning and classification process. Based on this vocabulary, a frame-worknamed“Enhanced Sentiment Analysis and Polarity Classification (sap)” is proposed. Training, testing and evaluation of the proposed sap are conducted on seven benchmark datasets from various domains. 10-fold cross validated accuracy, precision, recall, and f-measure results averaged over seven datasets for the proposed framework are 80.82%, 80.83%, 80.94% and 80.81% respectively. A notable performance improvement of 13.4% in accuracy, 14.2% in precision, 6.9%in recall and 11.1% in f-measure is observed on aver-age by evaluating the proposed sap against the baseline SWN classifier. State of the art performance comparison is conducted which also verifies the superiority of the proposed sap framework.