Toward understanding review usefulness: a case study on Yelp restaurants

The quality of online reviews has become a critical element for opinion-sharing-enabled platforms. Crowd- sourced feedback, such as votes, on previously shared reviews can provide signals about the quality of reviews. Prior studies examined some superficial features to explain the usefulness of votes to reviews, without accounting for the detailed information described in the review. By using 1052 restaurant reviews from Yelp, we extensively explore four types of features that are related to review content details, business neighborhoods, user profiles, and business profiles, to better understand the usefulness of voting on reviews. Our main findings indicated that based only on review voting, decisions made on whether a review is useful or not might lead to a biased result, since the review may receive votes due to other factors, regardless if the review discusses valuable aspects information of the business

Utilizing Review Summarization in a Spoken Recommendation System

In this paper we present a framework for spoken recommendation systems. To provide reliable recommendations to users, we incorporate a review summarization technique which extracts informative opinion summaries from grass-roots users‘ reviews. The dialogue system then utilizes these review summaries to support both quality-based opinion inquiry and feature- specific entity search. We propose a probabilistic language generation approach to automatically creating recommendations in spoken natural language from the text-based opinion summaries. A user study in the restaurant domain shows that the proposed approaches can effectively generate reliable and helpful recommendations in human-computer conversations.T-Party ProjectQuanta Computer (Firm

SES: Sentiment Elicitation System for Social Media Data

Abstract—Social Media is becoming major and popu-lar technological platform that allows users discussing and sharing information. Information is generated and man-aged through either computer or mobile devices by one person and consumed by many other persons. Most of these user generated content are textual information, as So-cial Networks(Facebook, LinkedIn), Microblogging(Twitter), blogs(Blogspot, Wordpress). Looking for valuable nuggets of knowledge, such as capturing and summarizing sentiments from these huge amount of data could help users make informed decisions. In this paper, we develop a sentiment identification system called SES which implements three dif-ferent sentiment identification algorithms. We augment basic compositional semantic rules in the first algorithm. In the second algorithm, we think sentiment should not be simply classified as positive, negative, and objective but a continuous score to reflect sentiment degree. All word scores are calculated based on a large volume of customer reviews. Due to the special characteristics of social media texts, we propose a third algorithm which takes emoticons, negation word posi-tion, and domain-specific words into account. Furthermore, a machine learning model is employed on features derived from outputs of three algorithms. We conduct our experiments on user comments from Facebook and tweets from twitter. The results show that utilizing Random Forest will acquire a better accuracy than decision tree, neural network, and logistic regression. We also propose a flexible way to represent document sentiment based on sentiments of each sentence contained. SES is available online. Keywords-Social media, sentiment, rule, machine learning I

Bipolar rating scales: A survey and novel correlation measures based on nonlinear bipolar scoring functions

© 2017, Budapest Tech Polytechnical Institution. All rights reserved. A bipolar rating scale is a linearly ordered set with symmetry between elements considered as negative and positive categories. First, we present a survey of bipolar rating scales used in psychology, sociology, medicine, recommender systems, opinion mining, and sentiment analysis. We discuss different particular cases of bipolar scales and, in particular, typical structures of bipolar scales with verbal labels that can be used for construction of bipolar rating scales. Next, we introduce the concept of bipolar scoring function preserving linear ordering and the symmetry of bipolar scales, study its properties, and propose methods for construction of bipolar scoring functions. We show that Pearson’s correlation coefficient often used for analysis of relationship between profiles of ratings in recommender systems can be misleading if the rating scales are bipolar. Basing on the general methods of construction of association measures, we propose new correlation measures on bipolar scales free from the drawbacks of Pearson’s correlation coefficient. Our correlation measures can be used in recommender systems, sentiment analysis and opinion mining for analysis of possible relationship between opinions of users and their ratings of items