Abstract
Sentiment analysis is a branch of natural language processing, or machine learning methods. It becomes one of the most important sources in decision making. It can extract, identify, evaluate or otherwise characterizes from the online sentiments reviews. Although Bag-Of-Words model is the most widely used technique for sentiment analysis, it has two major weaknesses: using a manual evaluation for a lexicon in determining the evaluation of words and analyzing sentiments with low accuracy because of neglecting the language grammar effects of the words and ignore semantics of the words. In this paper, we propose a new technique to evaluate online sentiments in one topic domain and produce a solution for some significant sentiment analysis challenges that improves the accuracy of sentiment analysis performed. The proposed technique relies on the enhancement bag-of-words model for evaluating sentiment polarity and score automatically by using the words weight instead of term frequency. This technique also can classify the reviews based on features and keywords of the scientific topic domain. This paper introduces solutions for essential sentiment analysis challenges that are suitable for the review structure. It also examines the effects by the proposed enhancement model to reach higher accuracy.
Highlights
Sentiment Analysis (SA) [1] is a Natural Language Processing and Information Extraction task that aims to obtain researcher’s feelings expressed in positive or negative reviews or opinion by analyzing a big numbers of documents and papers [2]
The technique described in this paper proposes an approach to evaluate sentiment score at the word level
Our contributions include the enhancement of Bag-of-Words model on online scientific papers reviews and the incorporate contextual polarity and effect of sentiment analysis challenges to improve the sentiment accuracy
Summary
Sentiment Analysis (SA) [1] is a Natural Language Processing and Information Extraction task that aims to obtain researcher’s feelings expressed in positive or negative reviews or opinion by analyzing a big numbers of documents and papers [2]. We discuss the sentiment classification and evaluation on word level in one topic domain. The sentence is subjective, Wordlevel SA [13] will determine whether the review expresses positive or negative based on evaluate each word polarity related with the feature. We present a new technique which called Sentiment Analysis Of Online Papers "SAOOP" It can evaluate online sentiment reviews for research paper domain. This technique is a new technique which introduces an enhancement of Bag-of-words model to solve major weaknesses of the Bag-Of-Words model in sentiment analysis evaluation. It depends on the word level of sentiment analysis in one topic domain.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have