Publication Type : Conference Paper
Thematic Areas : Learning-Technologies
Publisher : IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE).
Source : IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), 2014, IEEE, Jaipur (2014)
ISBN : 9781479940417
Accession Number : 14631408
Keywords : Intensifier, objective words, opinion mining, Sentiment analysis, sentiment polarity, subjective words .
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Center : AmritaCREATE
Department : Computer Science
Year : 2014
Abstract : Sentiment analysis is a valuable knowledge resource to understand collective sentiments from the Web and helps make better informed decisions. Sentiments may be positive, negative or objective and the method of assigning sentiment weights to terms and sentences are important factors in determining the accuracy of the sentiment classification. We use standard methods such as Natural Language Processing, Support Vector Machines and SentiWordNet lexical resource. Our work aims at improving the sentiment classification by modifying the sentiment values returned by SentiWordNet for intensifiers based on the context to the semantic of the words related to the intensifier. We also reassign some of the objective words to either positive or negative sentiment. We test our sentiment classification method with product reviews of digital cameras gathered from Amazon and ebay and shows that our method improves the prediction accuracy.
Cite this Research Publication : Jasmine Bhaskar, Sruthi, K., and Prof. Prema Nedungadi, “Enhanced sentiment analysis of informal textual communication in social media by considering objective words and intensifiers”, in IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), 2014, Jaipur, 2014
