Sentiment Analysis: a Study on Product Features

Sentiment analysis is a technique to classify people's opinions in product reviews, blogs or social networks. It has different usages and has received much attention from researchers and practitioners lately. In this study, we are interested in product feature based sentiment analysis. In other words, we are more interested in identifying the opinion polarities (positive, neutral or negative) expressed on product features than in identifying the opinion polarities of reviews or sentences. This is termed as the product feature based sentiment analysis. Several studies have applied unsupervised learning to calculate sentiment scores of product features. Although many studies used supervised learning in document-level or sentence-level sentiment analysis, we did not come across any study that employed supervised learning to product feature based sentiment analysis. In this research, we investigated unsupervised and supervised learning by incorporating linguistic rules and constraints that could improve the performance of calculations and classifications. In the unsupervised learning, sentiment scores of product features were calculated by aggregating opinion polarities of opinion words that were around the product features. In the supervised learning, feature spaces that contained right features for product feature based sentiment analysis were constructed. To reduce the dimensions of feature spaces, feature selection methods, Information Gain (IG) and Mutual Information (MI), were applied and compared. The results show that (i) product features were good indicators in determining the polarity classifications of document or sentences; (ii) rule based features could perform well in supervised learning e; and (iii) IG performed better in document analysis, while MI performed better in sentence-level analysis. ACKNOWLEDGEMENTS I have been very lucky to study Management Information System at University of Nebraska-Lincoln, where I have the opportunity to gain much knowledge from a number of outstanding professors. Firsthand, I would like to express my sincere gratitude to my academic advisor Professor Keng L. Siau for guiding me this thesis. His insights and guidance over these two years have been invaluable to me. Two years ago, I had no idea what I would do after I graduate. But now, under the guidance of Professor Siau, I have acquired a lot of skills on Business Intelligence and Text mining, and I am very clear and confident about what I will do in the future. I would also like to thank Professor Fiona Nah and Professor Sidney Davis for serving as my committee members, reading my thesis, attending my defense and giving me valuable …