Opinion Polarity Identification through Adjectives

TL;DR

This paper presents a method for determining opinion polarity in reviews by analyzing adjectives, achieving higher accuracy than naive Bayesian classifiers, thus aiding in efficient opinion mining from web sources.

Contribution

The paper introduces a novel adjective-based approach for opinion polarity detection that outperforms traditional naive Bayesian classifiers in accuracy.

Findings

Achieved 73% accuracy in polarity identification.

Outperformed naive Bayesian classifiers (58%-64%).

Effective for large-scale opinion mining.

Abstract

"What other people think" has always been an important piece of information during various decision-making processes. Today people frequently make their opinions available via the Internet, and as a result, the Web has become an excellent source for gathering consumer opinions. There are now numerous Web resources containing such opinions, e.g., product reviews forums, discussion groups, and Blogs. But, due to the large amount of information and the wide range of sources, it is essentially impossible for a customer to read all of the reviews and make an informed decision on whether to purchase the product. It is also difficult for the manufacturer or seller of a product to accurately monitor customer opinions. For this reason, mining customer reviews, or opinion mining, has become an important issue for research in Web information extraction. One of the important topics in this research area is the identification of opinion polarity. The opinion polarity of a review is usually expressed with values 'positive', 'negative' or 'neutral'. We propose a technique for identifying polarity of reviews by identifying the polarity of the adjectives that appear in them. Our evaluation shows the technique can provide accuracy in the area of 73%, which is well above the 58%-64% provided by naive Bayesian classifiers.