User-generated texts such as reviews and social media are valuable sources of information. Online reviews are important assets for users to buy a product, see a movie, or make a decision. Therefore, rating of a review is one of the reliable factors for all users to read and trust the reviews. This paper analyzes the texts of the reviews to evaluate and predict the ratings. Moreover, we study the effect of lexical features generated from text as well as sentimental words on the accuracy of rating prediction. Our analysis show that words with high information gain score are more efficient compared to words with high TF-IDF value. In addition, we explore the best number of features for predicting the ratings of the reviews.
in @cite_33 suggested an automated assessment for the review helpfulness by considering the length of the review as the most useful feature compared to the other ones. The main contribution of this paper is: a) using helpfulness score to implement an automatic computational model to rank the reviews, and b) leveraging various features in the reviews such as structural, lexical, syntactic, semantic, and reviews’ starts to predict the helpfulness score of the reviews. For products such as MP3 players and cameras, the model achieved correlation coefficient scores of 0.656 and 0.604. The detailed analysis of features showed that length of the reviews, unigrams, and product rating were the most helpful ones in the prediction and structural and syntactic features had no significant impact @cite_44 .
@cite_44 @cite_33
2112744748 2087294982
With the flourish of the Web, online review is becoming a more and more useful and important information resource for people. As a result, automatic review mining and summarization has become a hot research topic recently. Different from traditional text summarization, review mining and summarization aims at extracting the features on which the reviewers express their opinions and determining whether the opinions are positive or negative. In this paper, we focus on a specific domain - movie review. A multi-knowledge based approach is proposed, which integrates WordNet, statistical analysis and movie knowledge. The experimental results show the effectiveness of the proposed approach in movie review mining and summarization. User-supplied reviews are widely and increasingly used to enhance e-commerce and other websites. Because reviews can be numerous and varying in quality, it is important to assess how helpful each review is. While review helpfulness is currently assessed manually, in this paper we consider the task of automatically assessing it. Experiments using SVM regression on a variety of features over Amazon.com product reviews show promising results, with rank correlations of up to 0.66. We found that the most useful features include the length of the review, its unigrams, and its product rating.