Why some online product reviews have no usefulness rating?

Combining econometric analysis with text mining techniques, this study attempts to explore why some online product reviews have no usefulness rating through examining review posting time and text features. Later posting time may reduce the probability of some online reviews being seen and thus lead to their being not rated for usefulness. Besides, the neutral diagnosticity of reviews reflected from the text features may cause difficulty for readers to judge and evaluate the usefulness of these reviews. Our study finds that, though not being seen due to later posting time obviously explains no usefulness rating for some online reviews, the neutral diagnosticity of these reviews is also an important and non-neglectable cause for their having no usefulness rating. Further, we identify the text features which may lead to the neutral diagnosticity of the review. Our study has implications for online product reviews website managers in identifying and dismissing the reviews with no usefulness rating to improve readers’ information retrieving efficiency and also for reviewers in improving the diagnosticity of their reviews

Ma et al.: An LDA and Synonym Lexicon Based Approach to Product Feature Extraction AN LDA AND SYNONYM LEXICON BASED APPROACH TO PRODUCT FEATURE EXTRACTION FROM ONLINE CONSUMER PRODUCT REVIEWS

ABSTRACT Consumers are increasingly relying on other consumers' online reviews of features and quality of products while making their purchase decisions. However, the rapid growth of online consumer product reviews makes browsing a large number of reviews and identifying information of interest time consuming and cognitively demanding. Although there has been extensive research on text review mining to address this information overload problem in the past decade, the majority of existing research mainly focuses on the quality of reviews and the impact of reviews on sales and marketing. Relatively little emphasis has been placed on mining reviews to meet personal needs of individual consumers. As an essential first step toward achieving this goal, this study proposes a product feature-oriented approach to the analysis of online consumer product reviews in order to support feature-based inquiries and summaries of consumer reviews. The proposed method combines LDA (Latent Dirichlet Allocation) and a synonym lexicon to extract product features from online consumer product reviews. Our empirical evaluation using consumer reviews of four products shows higher effectiveness of the proposed method for feature extraction in comparison to association rule mining

Detecting sentiment orientation using supervised learning

Opinion mining is one of the important tasks of natural language processing. Sentiment analysis classify the data into summarization and opinions about the product. The proposed system is based on phrase-level to examine customer reviews. Proposed system extract the features from online reviews and before extracting review it apply pre processing step to each individual sentence of review. This system extract the implicit and explicit features of review. It uses the Apriori algorithm for extracting frequent features. Supervised Naive Bayes determine orientation of extracted aspect Orientation of product review is identified by natural language processing

Mining Pros and Cons of Product Features from Online Reviews: Aspect-Sentiment Analysis on Textual Reviews

Online reviews have become the modern-day referral, which shapes consumers’ perceptions of products and thus influences product sales performance in the digital economy (Blanco, Sarasa, & Sanclemente, 2010). Prior literature suggests that online consumers\u27 textual information significantly affects product performance and has important strategic value for organizations (Zhou et al., 2018). Sentiment analysis is used to identify the positive and negative tone of textual information (Hu, Bose, Koh, & Liu, 2012) and has become a primary application of analytics when researchers investigate how user-generated information influences product performance. However, most existing online review studies conduct sentiment analysis at the review level, which focuses on identifying the valence of an individual message or review (e.g., Hu et al., 2012; Wu, Huang, & Zhao, 2019), rather than the feature-based, which aims to reveal prior customers’ evaluation of product features in reviews (e.g., Wang, Lu, & Tan, 2018). Since consumers’ fundamental purpose in reading textual reviews is to obtain details about the product attributes’ pros and cons (Xu, 2019), conducting sentiment analysis at review-level fails to measure customer satisfaction concerning each attribute of products or services and does not match the mechanism of how online text reviews are consumed. Therefore, feature-based review-level sentiment analysis better reflects the actual value of textual information in the digital economy

The Investigation of Multiple Product Rating Based on Data Mining Approaches

Ratings and product reviews could be considered as one of the main features determining the quality of a product in online store systems, especially in deciding whether to place a product as part of an online store's inventory. Online vendors are attracted by product reviews and ratings in order to study on potential products and related predictions. In this way, different machine learning algorithms such as Support Vector Machine, Bayesian Networks, Random Forests and Logistic Regression are investigated. The performance of each model is evaluated using accuracy, sensitivity and F1 score on the data from amazon online store website, 1996 to 2014. It is noteworthy to mention that the results of this paper can be used as an initial input to long-term product rating predictions. Keywords: Rating, Machine Learning Algorithm, Text mining, Classification, Resampling DOI: 10.7176/CEIS/10-5-03 Publication date:June 30th 201

Consumer Stated Preference for Acer Laptop from Online Reviews

Consumer preference is a hot topic in the domain of marking management and e-commerce. Many previous studies have been conducted in this field. Whereas, there are rarely studies building on the particular commodity such as laptop. Therefore, this study explores comprehensive features that affect consumer preference for laptops by mining the online reviews. Firstly, we collect 6531 online reviews for Acer laptop from Amazon.cn and code these reviews with Nvivo10. Secondly, we develop a feature-based consumer preference model named MCPL based on the review text analysis. Considering the data imbalance of the collected 6531 product reviews, we adopt a random cluster sampling method to extract 50 groups with 100 samples per group. Then the correspondent regression analyses are conducted for the 50 groups of reviews. Finally, the meta-analysis is creatively conducted to integrate the multiple liner regression results of different groups. According to the result of meta-analysis, we demonstrate dominant features on behalf of the consumer preference of laptop and draw practical implications for enterprise competition strategies to facilitate product design or improvement

Automatic Summarization in Chinese Product Reviews

With the increasing number of online comments, it was hard for buyers to find useful information in a short time so it made sense to do research on automatic summarization which fundamental work was focused on product reviews mining. Previous studies mainly focused on explicit features extraction whereas often ignored implicit features which hadn't been stated clearly but containing necessary information for analyzing comments. So how to quickly and accurately mine features from web reviews had important significance for summarization technology. In this paper, explicit features and “feature-opinion” pairs in the explicit sentences were extracted by Conditional Random Field and implicit product features were recognized by a bipartite graph model based on random walk algorithm. Then incorporating features and corresponding opinions into a structured text and the abstract was generated based on the extraction results. The experiment results demonstrated the proposed methods outpreferred baselines

A Survey on Classification Techniques for Feature-Sentiment Analysis

As use of internet and its application are growing exponentially; the e-commerce business i.e. online purchase is proportionately swelling in the world. The e-commerce websites and similar service providing websites are providing a rich variety of product and service to be sold. As the quality of service and product/goods has much effect on its sell, the websites nowadays tends to have public opinion on the product in the form of feedback; we can name it as reviews. These reviews provide much information about the service/product as the customers are encouraged to write their reviews cum assessments about the product, more precisely saying, customer writes their view about product’s specifications or product’s features. These unrestricted or restricted opinions from public can then be considered by the customers and vendor to make the required design/engineering/production changes to the product to upsurge its quality. The Feature Mining along with Sentiment Analysis techniques can be applied to achieve product’s feature and public opinion on these features. Here in this paper we are interestingly motivated by the scenario as discussed above. We had a survey on the different methods cum techniques that can be usually used to extract products/service features and categorizing those feature along with the sentiment classification on the determined features which is part of Machine learning. The public opinions can be classified as positive, negative and neutral sentimentalities. Research area ‘Data Mining’ has proven its importance with its rich set of Machine Learning Algorithms which in turn can be used as Sentiment or Opinion Classifier. After evaluating feature-sentiment techniques, we then studied the feature classification/categorizing by using its overall sentiment and influence on the product/service sell. DOI: 10.17762/ijritcc2321-8169.15079

Where Does My Product Stand? A Social Network Perspective on Online Product Reviews

Customer reviews often include comparative comments on competing products. Adopting the The Strength of Weak Ties theory, we build a product social network around “strong tie” and “weak tie” entities. By performing text mining on comparative customer reviews collected from Amazon, we successfully identify strong and weak ties in a product network and compute the strength of these ties. Utilizing these network properties, we generate network graphs based on different product features and discover the underlying competitive relationships among them. In particular, our regression analysis shows that the strength of ties positively contributes to the review rating of a product and the strength of weak ties plays a more significant role than the strength of strong ties. These results will benefit vendors in online market to discover potential competitors, effectively tailor their marketing and product development efforts, and better position their products to increase profit and explore new market opportunities

Identifying Product Features from Customer Reviews using Lexical Concordance

Abstract: Automatic extraction of features from unstructured text is one of the challenging problems of Opinion Mining. The trend of getting products and services reputation from online resources such as web blogs and customer feedback is increasing day by day. Therefore efficient system is required to automatically extract products features and the opinion of consumers about all aspects of the products. In this study our focus is on extraction of product features from customer reviews. We have proposed a concordance based technique for automatic extraction of features of product from customer reviews. In our proposed technique we extract patterns of lexical terms using concordance for candidate features extraction and identify features by grouping. The proposed grouping algorithm is used to remove irrelevant features. We conducted experiments on different products reviews and compared our results with existing methods. From empirical results we proved the validity of the proposed method