Implications of Erroneous Product Reviews by Product-Enthusiast Communicators

It is revealed through a between-subjects experiment that “blogger error” produces blameworthiness cognitions as well as specific affective states that together facilitate intentions in offended blog readers to engage in revenge-seeking behaviors directed at the offending blogger. Blogger error represents a negative occurrence to offended blog readers who, depending on the blame they assign and feelings of anger and dissatisfaction they experience, may seek to inflict harm on the offending blogger in the forms of negative word-of-mouth communication and online public complaining behaviors. Word-of-mouth marketing, a growing managerial practice that involves material relationships between consumers and organizations which must be disclosed to audience members, can be harmful to bloggers who, whether intentionally or not, publish erroneous content on their blogs

Research Directions, Challenges and Issues in Opinion Mining

Rapid growth of Internet and availability of user reviews on the web for any product has provided a need for an effective system to analyze the web reviews. Such reviews are useful to some extent, promising both the customers and product manufacturers. For any popular product, the number of reviews can be in hundreds or even thousands. This creates difficulty for a customer to analyze them and make important decisions on whether to purchase the product or to not. Mining such product reviews or opinions is termed as opinion mining which is broadly classified into two main categories namely facts and opinions. Though there are several approaches for opinion mining, there remains a challenge to decide on the recommendation provided by the system. In this paper, we analyze the basics of opinion mining, challenges, pros & cons of past opinion mining systems and provide some directions for the future research work, focusing on the challenges and issues

PENGARUH PRODUCT REVIEW DAN GARANSI DALAM MEMBENTUK BRAND AWARENESS PADA LAPTOP AXIOO PONGO

Brand awareness is one of the key aspects in the success of a product. Brand awareness is one of the foundations for forming a brand image in the minds of consumers. This research aims to determine the influence of product reviews on brand awareness on Axioo Pongo laptops, determine the influence of warranties on brand awareness on Axioo Pongo laptops, and determine the influence of product reviews and warranties on brand awareness on Axioo Pongo laptops. This research was conducted on Instagram followers of the Calosastore Malang. The method used in this research is quantitative. The approach used in this research is a descriptive approach. This research uses multiple linear regression analysis methods. The tool used to test instruments and process data is the SPSS 26 program. The results of this research are that product reviews have a partially significant effect on Axioo Pongo brand awareness, warranties have a partially significant effect on Axioo Pongo brand awareness, and product reviews and warranties have a partially significant effect. simultaneously on Axioo Pongo brand awareness

Using Argument-based Features to Predict and Analyse Review Helpfulness

We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidences-conclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201

Identifying leading indicators of product recalls from online reviews using positive unlabeled learning and domain adaptation

Consumer protection agencies are charged with safeguarding the public from hazardous products, but the thousands of products under their jurisdiction make it challenging to identify and respond to consumer complaints quickly. From the consumer's perspective, online reviews can provide evidence of product defects, but manually sifting through hundreds of reviews is not always feasible. In this paper, we propose a system to mine Amazon.com reviews to identify products that may pose safety or health hazards. Since labeled data for this task are scarce, our approach combines positive unlabeled learning with domain adaptation to train a classifier from consumer complaints submitted to the U.S. Consumer Product Safety Commission. On a validation set of manually annotated Amazon product reviews, we find that our approach results in an absolute F1 score improvement of 8% over the best competing baseline. Furthermore, we apply the classifier to Amazon reviews of known recalled products; the classifier identifies reviews reporting safety hazards prior to the recall date for 45% of the products. This suggests that the system may be able to provide an early warning system to alert consumers to hazardous products before an official recall is announced

Using Argument-based Features to Predict and Analyse Review Helpfulness

We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidences-conclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201