Identifying Major Tasks from On-line Reviews

© 2017 The Authors. Published by Elsevier B.V. Many e-commerce websites allow customers to provide reviews that reflect their experiences and opinions about the business\u27s products or services. Such published reviews potentially benefit the business\u27s reputation, improve both current and future customers\u27 trust in the business, and accordingly improve the business. Negative reviews can inform the merchant of issues that, when addressed, also improve the business. However, when reviews reflect negative experiences and the merchant fails to respond, the business faces potential loss of reputation, trust, and damage. We present the Sentiminder system that identifies reviews with negative sentiment, organizes them, and helps the merchant develop a plan with an end date by which issues will be addressed. In this paper we address the problem of quickly finding subtasks in a large set of reviews, which may help the merchant to identify, from the set of reviews, subtasks that need to be addressed. We do this by identify nouns that frequently occur only in the reviews with negative sentiment

Topic Discovery of Online Course Reviews Using LDA with Leveraging Reviews Helpfulness

Despite the popularity of the Massive Open Online Courses, small-scale research has been done to understand the factors that influence the teaching-learning process through the massive online platform. Using topic modeling approach, our results show terms with prior knowledge to understand e.g.: Chuck as the instructor name. So, we proposed the topic modeling approach on helpful subjective reviews. The results show five influential factors: “learn easy excellent class program”, “python learn class easy lot”, “Program learn easy python time game”, and “learn class python time game”. Also, research results showed that the proposed method improved the perplexity score on the LDA model

Predicting Product Review Helpfulness Using Machine Learning and Specialized Classification Models

In this paper we focus on automatically classifying product reviews as either helpful or unhelpful using machine learning techniques, namely, SVM classifiers. Using LIBSVM and a set of Amazon product reviews from 25 product categories, we train models for each category to determine if a review will be helpful or unhelpful. Previous work has focused on training one classifier for all reviews in the data set, but we hypothesize that a distinct model for each of the 25 product types available in the review dataset will improve the accuracy of classification. ! Furthermore, we develop a framework to inform authors on the fly if their review is predicted to be of great use (helpful) to other readers, with the assumption that authors are more likely to rethink their review post and amend it to be of maximum utility to other readers when given some feedback on whether or not it will be found helpful or unhelpful. ! Using past research as a baseline, we find that specialized SVM classifiers outperform higher level models of review helpfulness prediction

Social media and e-commerce: A scientometrics analysis

he purpose of this research is to investigate the status and the evolution of the scientific studies on the effect of social networks on e-commerce. The study seeks to address the status of a set of scientific productions of researchers in the world indexed in Scopus based on scientometrics indicators. In total, 1926 articles were found and the collected data were analyzed using quantitative and qualitative indicators of scientometrics with bibliometrix R software package. The findings show that researches have grown exponentially since 2009 and the trend has continued at relatively stable rates. Thematic analysis shows that the subject had a significant but not well-developed research field. There is a high rate of cooperation with a rich research network among institutions in United States, European and Asian countries. Studies also show that research interest in this area is prevalent in developed countries. In addition, the lack of funds and complex analytical tools may be due to lack of studies in developing countries, especially in Africa. The study of the global trend of research through scientometrics helps managers and researchers in identifying countries and institutions with the greatest potential for scientific production, which allows them to develop their professions

Detecting Popularity of Ideas and Individuals in Online Community

Research in the last decade has prioritized the effects of online texts and online behaviors on user information prediction. However, the previous research overlooks the overall meaning of online texts and more detailed features about users’ online behaviors. The purpose of the research is to detect the adopted ideas, the popularity of ideas, and the popularity of individuals by identifying the overall meaning of online texts and the centrality features based on user’s online interactions within an online community. To gain insights into the research questions, the online discussions on MyStarbucksIdea website is examined in this research. MyStarbucksIdea had launched since 2008 that encouraged people to submit new ideas for improving Starbuck’s products and services. Starbucks had adopted hundreds of ideas from this crowdsourcing platform. Based on the example of the MyStarbucksIdea community, a new document representation approach, Doc2Vec, synthesized with the users’ centrality features was unitized in this research. Additionally, it also is essential to study the surface-level features of online texts, the sentiment features of online texts, and the features of users’ online behaviors to determine the idea adoption as well as the popularity of ideas and individuals in the online community. Furthermore, supervised machine learning approaches, including Logistic Regression, Support Vector Machine, and Random Forest, with the adjustments for the imbalanced classes, served as the classifiers for the experiments. The results of the experiments showed that the classifications of the idea adoption, the popularity of ideas, and the popularity of individuals were all considered successful. The overall meaning of idea texts and user’s centrality features were most accurate in detecting the adopted ideas and the popularity of ideas. The overall meaning of idea texts and the features of users’ online behaviors were most accurate in detecting the popularity of individuals. These results are in accord with the results of the previous studies, which used behavioral and textual features to predict user information and enhance the previous studies\u27 results by providing the new document embedding approach and the centrality features. The models used in this research can become a much-needed tool for the popularity predictions of future research

Three Essays on Opinion Mining of Social Media Texts

This dissertation research is a collection of three essays on opinion mining of social media texts. I explore different theoretical and methodological perspectives in this inquiry. The first essay focuses on improving lexicon-based sentiment classification. I propose a method to automatically generate a sentiment lexicon that incorporates knowledge from both the language domain and the content domain. This method learns word associations from a large unannotated corpus. These associations are used to identify new sentiment words. Using a Twitter data set containing 743,069 tweets related to the stock market, I show that the sentiment lexicons generated using the proposed method significantly outperforms existing sentiment lexicons in sentiment classification. As sentiment analysis is being applied to different types of documents to solve different problems, the proposed method provides a useful tool to improve sentiment classification. The second essay focuses on improving supervised sentiment classification. In previous work on sentiment classification, a document was typically represented as a collection of single words. This method of feature representation suffers from severe ambiguity, especially in classifying short texts, such as microblog messages. I propose the use of dependency features in sentiment classification. A dependency describes the relationship between a pair of words even when they are distant. I compare the sentiment classification performance of dependency features with a few commonly used features in different experiment settings. The results show that dependency features significantly outperform existing feature representations. In the third essay, I examine the relationship between social media sentiment and stock returns. This is the first study to test the bidirectional effects in this relationship. Based on theories in behavioral finance research, I speculate that social media sentiment does not predict stock return, but rather that stock return predicts social media sentiment. I empirically test a set of research hypotheses by applying the vector autoregression (VAR) model on a social media data set, which is much larger than those used in previous studies. The hypotheses are supported by the results. The findings have significant implications for both theory and practice

Review on recent advances in information mining from big consumer opinion data for product design

In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design

A WEB PERSONALIZATION ARTIFACT FOR UTILITY-SENSITIVE REVIEW ANALYSIS

Online customer reviews are web content voluntarily posted by the users of a product (e.g. camera) or service (e.g. hotel) to express their opinions about the product or service. Online reviews are important resources for businesses and consumers. This dissertation focuses on the important consumer concern of review utility, i.e., the helpfulness or usefulness of online reviews to inform consumer purchase decisions. Review utility concerns consumers since not all online reviews are useful or helpful. And, the quantity of the online reviews of a product/service tends to be very large. Manual assessment of review utility is not only time consuming but also information overloading. To address this issue, review helpfulness research (RHR) has become a very active research stream dedicated to study utility-sensitive review analysis (USRA) techniques for automating review utility assessment. Unfortunately, prior RHR solution is inadequate. RHR researchers call for more suitable USRA approaches. Our current research responds to this urgent call by addressing the research problem: What is an adequate USRA approach? We address this problem by offering novel Design Science (DS) artifacts for personalized USRA (PUSRA). Our proposed solution extends not only RHR research but also web personalization research (WPR), which studies web-based solutions for personalized web provision. We have evaluated the proposed solution by applying three evaluation methods: analytical, descriptive, and experimental. The evaluations corroborate the practical efficacy of our proposed solution. This research contributes what we believe (1) the first DS artifacts to the knowledge body of RHR and WPR, and (2) the first PUSRA contribution to USRA practice. Moreover, we consider our evaluations of the proposed solution the first comprehensive assessment of USRA solutions. In addition, this research contributes to the advancement of decision support research and practice. The proposed solution is a web-based decision support artifact with the capability to substantially improve accurate personalized webpage provision. Also, website designers can apply our research solution to transform their works fundamentally. Such transformation can add substantial value to businesses