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.
Highlights
World Wide Web has become the most popular communication platforms to the public reviews, opinions, comments and sentiments about products, places, scientific books or papers and to daily text reviews
NLPS technique which is predicting the sentiment of reviews based on a recursive model
We examine the average result analysis of the two big data set that spirited into three data sets, that illustrate the highest average results with sentiment score of the proposed sentiment analysis of online papers (SAOOP) technique NLPS and the lowest one is a Natural Language Toolkit-Text processing” (NLTK) Technique
Summary
World Wide Web (www) has become the most popular communication platforms to the public reviews, opinions, comments and sentiments about products, places, scientific books or papers and to daily text reviews. The number of active user bases and the size of their reviews created daily on online websites are massive. According to a new survey conducted by Dimensional Research, April 2013: 90% of customer’s decisions depends on Online Reviews [3]. According to 2013 Study [4]: 79% of customer’s confidence is based on online personal recommendation reviews. A large number of studies and research have monitored the trending new research increasing year by year. In this work, trying to achieve trusted scientific reviews evaluation to be useful for researchers and facilitate the selection of the suitable papers
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