Abstract-Sentiment analysis or opinion mining is used to automate the detection of subjective information such as opinions, attitudes, emotions, and feelings. Hundreds of thousands care about scientific research and take a long time to select suitable papers for their research. Online reviews on papers are the essential source to help them. The reviews save reading time and save papers cost. This paper proposes a new technique to analyze online reviews. It is called sentiment analysis of online papers (SAOOP). SAOOP is a new technique used for enhancing bag-of-words model, improving the accuracy and performance. SAOOP is useful in increasing the understanding rate of review's sentences through higher language coverage cases. SAOOP introduces solutions for some sentiment analysis challenges and uses them to achieve higher accuracy. This paper also presents a measure of topic domain attributes, which provides a ranking of total judging on each text review for assessing and comparing results across different sentiment techniques for a given text review. Finally, showing the efficiency of the proposed approach by comparing the proposed technique with two sentiment analysis techniques. The comparison terms are based on measuring accuracy, performance and understanding rate of sentences.
PROPOSED TECHNIQUE In this paper, we propose the enhancement bag-of-words (BOW) model on sentiment analysis by presenting a new technique is called Sentiment analysis of online papers "SAOOP" REF .
Online Paper Review Analysis
International Journal of Advanced Computer Science and Applications
Computer Science