Online reviews as first class artifacts in mobile app development.

This paper introduces a framework for developing mobile apps. The framework relies heavily on app stores and, particularly, on online reviews from app users. The underlying idea is that app stores are proxies for users because they contain direct feedback from them. Such feedback includes feature requests and bug reports, which facilitate design and testing respectively. The framework is supported by MARA, a prototype system designed to automatically extract relevant information from online reviews

Context-aware Helpfulness Prediction for Online Product Reviews

Modeling and prediction of review helpfulness has become more predominant due to proliferation of e-commerce websites and online shops. Since the functionality of a product cannot be tested before buying, people often rely on different kinds of user reviews to decide whether or not to buy a product. However, quality reviews might be buried deep in the heap of a large amount of reviews. Therefore, recommending reviews to customers based on the review quality is of the essence. Since there is no direct indication of review quality, most reviews use the information that ''X out of Y'' users found the review helpful for obtaining the review quality. However, this approach undermines helpfulness prediction because not all reviews have statistically abundant votes. In this paper, we propose a neural deep learning model that predicts the helpfulness score of a review. This model is based on convolutional neural network (CNN) and a context-aware encoding mechanism which can directly capture relationships between words irrespective of their distance in a long sequence. We validated our model on human annotated dataset and the result shows that our model significantly outperforms existing models for helpfulness prediction.Comment: Published as a proceeding paper in AIRS 201

A Survey on Sentiment Mining

In past days before putting money into any product people used to ask judgment to their family, friend circle and colleagues and then they take the decision. In today’s world there is a boom of World Wide Web, enormous amount of data is available on internet so while purchasing a product instead of asking to people customer take decisions by analyzing electronic text. As the growth of e-commerce crowds of people encouraged to write their opinion about numerous merchandise in the form of statements/comments on countless sites like facebook,flipkart,snapdeal,amazon,bloggres,twiter,etc.This comments are the sentiments about the services expressed by users and they are categorized into positive, negative and neutral. Different techniques are use for summarizing reviews like Information Retrieval, Text Mining Text Classification, Data Mining, and Text Summarizing. Countless people write their sentiments on plenty of sites. These comments are written in random order so it may cause trouble in usefulness of the information. If someone wants to find out the impact of the usability of any product then he has to manually read all the sentiments and then classify it, which is practically burdensome task. Sentiment mining is playing major role in data mining; it is also referred as sentiment analysis. This field helps to analyze and classify the opinion of users. In this paper we will discuss various techniques, applications and challenges face by the sentiment mining

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

UNDERSTANDING THE MASSIVE ONLINE REVIEWS: A NOVEL REPRESENTATIVE SUBSET EXTRACTION METHOD

Online review hasalready been recognized as an important sales assistant for consumers to make their purchase decision. However, with the rapid development of electronic commerce,overwhelming informationoverloads and review manipulation make consumers lost in ocean of reviews and face huge cognitive stress. To address this issue, different types of online review have been developed by online marketplaces. Especially, except traditional types of online reviews (positive, neutral and negative), several new types of online review (review with picture and additional review) do not only contain plain text, but also pictures. Consumers could attach additional reviews to the original reviews to further share their experience sometimes later. Few studies have focused on which types of online reviews are able to influence consumers’ decisions more efficiently. Especially, research on new types of reviews is still unanswered.Using data from Taobao.com, the biggest electronic marketplace in China,this study conducts an empirical investigation to bridge the gap. Weinvestigatethat whether and howtraditional text reviewsand new types of reviews influence consumers’ purchase decision making. The results show that under the context of information overload and review manipulation, traditional reviewsare still influential, but less effective than new types of reviews. Although review with picture and additional review don’t show valence directly, they present more reliable references towards product quality and attract consumers’ attention more efficiently.And it is more interesting that new types of online review provide an effective channel for consumers to alleviate their dissatisfaction to effect potential consumers purchase decision making. The findings of this study can provide useful implications for researchers by highlighting the roles of different types of online review in consumers’ decision making. Also, the empirical investigation in this paper will remind business vendors to focus on online reviews especially new types of online reviews and conduct targeted marketing strategies to increase competitive advantage and improve their sales performance

An Ontology Artifact for Information Systems Sentiment Analysis

As companies and organizations increasingly rely on on-line, user-supplied data to obtain valuable insights into their operations, sentiment analysis of textual data has proven to be a most valuable resource. To understand how sentiment analysis can be used effectively, it is important to identify what types of sentiment analysis could be employed during the analysis of a given situation. This research proposes an Information Systems Sentiment Ontology, the purpose of which is to provide a basis for mining and understanding sentiment, specifically from text provided by customers as online content. The Information Systems Sentiment Ontology is developed by analyzing the literature on emotion, sentiment analysis, and ontology development and from prior research on online forum analysis. A traditional design science approach is followed to the ontology development. Details on the creation and application of the ontology artifact are provided

Classification of Smartphone Application Reviews Using Small Corpus Based on Bidirectional LSTM Transformer

This paper provides the classification of the review texts on a smartphone application posted on social media. We propose a high performance binary classification method (positive/negative) of review texts, which uses the bidirectional long short-term memory (biLSTM) self-attentional Transformer and is based on the distributed representations created by unsupervised learning of a manually labelled small review corpus, dictionary, and an unlabeled large review corpus. The proposed method obtained higher accuracy as compared to the existing methods, such as StarSpace or the Bidirectional Encoder Representations from Transformer (BERT)