Moderating Effects of Time-Related Factors in Predicting the Helpfulness of Online Reviews: a Deep Learning Approach

Given the importance of online reviews, as shown by extensive research, we address the problem of predicting the helpfulness of online product reviews by developing a comprehensive research model guided by the theoretical foundations of signaling and social influence theories. We use review order and time interval to incorporate the moderating effects of the time-related variable on the reviewer’s valuation of products and the related details they provide. Applying deep learning techniques in text processing and model building on a dataset of 239297 reviews, the empirical findings represent strong support of the proposed approach and show its superior performance in predicting review helpfulness compared to current approaches. This research contributes to theory by analyzing online reviews from the points of two well-known information processing theories and contributes to practice by developing a model to sort the newly posted reviews

Emotions Trump Facts: The Role of Emotions in on Social Media: A Literature Review

Emotions are an inseparable part of how people use social media. While a more cognitive view on social media has initially dominated the research looking into areas such as knowledge sharing, the topic of emotions and their role on social media is gaining increasing interest. As is typical to an emerging field, there is no synthesized view on what has been discovered so far and - more importantly - what has not been. This paper provides an overview of research regarding expressing emotions on social media and their impact, and makes recommendations for future research in the area. Considering differentiated emotion instead of measuring positive or negative sentiment, drawing from theories on emotion, and distinguishing between sentiment and opinion could provide valuable insights in the field

How Do Consumers Identify Useful Review Information in a Social Media Environment?

The popularity of social network services (SNS) provides consumers with new channels to obtain information, and the convenience and diversity of information help consumers reduce the uncertainty and risk in online shopping. However, with the development of the internet, increasing information is available to consumers, and the information overload of online reviews (ORs) creates higher requirements for consumer information screening. Therefore, how to quickly and accurately identify useful information becomes important. There is much literature discussing the usefulness of online word of mouth and ORs, but these studies generally explore information selection and judgment in the traditional business to consumer environment and do not specifically explore social shopping in SNS. In addition, the existing results often only consider the quality factor of ORs and ignore the influence of consumers’ personal choice preferences and the characteristics of OR publishers on consumer behavior. From this, based on the theory of planned behavior and the online trust model, this paper determines the relevant factors and frameworks that affect the usefulness of ORs in the SNS environment. We start from the perspectives of reviewer-related and information-related aspects. Partial least squares structural equation modeling (PLS-SEM) was used to analyze 237 samples for eight factors: credibility, social distance, evaluation (positive and negative), information quality (accuracy, timeliness, and integrity), and presentation. This study enriches the theory of OR usefulness in the SNS environment and provides a reference for the online marketing of enterprises

Sound Perspectives: Sentiment Analysis of Residents of the Puget Sound On Indigenous Fisheries

Indigenous management and coordinated co-management of natural resources is an emotionally filled topic, especially for those within the Puget Sound region of Washington, but has yet to be studied in depth from the emotional perspective of residents. This research used sentiment analysis to find the overarching sentiment of indigenous fisheries, how it varies by demographics and level of trust in governance, and frequency of reported fishing. I also sought to identify which primary emotions were most evoked by respondents. From this I found that when people chose to make a free comment, the sentiments were predominantly negative. Demographics did not vary significantly between those evoking positive or negative sentiment, although the few people of color within the sample evoked positive sentiment. Primary emotions evoked by respondents within the negative sentiment group were sadness and disgust, while those within the positive sentiment group expressed trust and joy. These primary emotions of the sentiments groups are directly opposing according to the psychology of emotion theory. There were various words respondents used frequently when expressing themselves, and these indicating words matched with their sentiment can lead to a better understanding of the misunderstandings and misconceptions of these two user groups. Utilizing these insights of the respondents emotions underlying this topic can lead to more effective ways to communicate co-management and improve perceptions between sovereign indigenous nations and non-indigenous fishing groups.Key Words: Sentiment analysis, Qualitative study, Puget Sound, Salmon, Native, Indigenous, Fisherie

A model of argument quality for information adoption in e-commerce review platform

The viral nature the content of the Web has transformed the landscape of e-Commerce review platforms to be in a state of constant growth. Similarly, the prominent features of these platforms have been recognized to be among the dominant factors in shaping online consumer behavior. Nonetheless, in this regard, if the review platform returns too many reviews, and the reviews are presented in non-relevant manner, in which this may be cumbersome and time-consuming for consumers. Therefore, identifying credible reviews that contain valuable information has becomes increasingly important for online businesses. The main research question to be addressed in this study is to determine on how can a model be developed to improve the argument quality perceptions in the adoption of online reviews across e-Commerce review platform. Subsequently, the main objective to be achieved is to develop a model of argument quality for review‘s adoption in the e-Commerce review platform. The potential effects of consumer relevance judgment from information retrieval perspective have been considered, which include perceived informative and affective relevance in developing the research model by using Elaboration Likelihood Model (ELM). A quantitative research method has been applied to test and validate the propose research model. The response data from 238 valid respondents was analyzed using the Partial Least Square Structural Modelling (PLS-SEM) technique. The findings from the results indicate that content novelty, content topicality, content similarity, content tangibility and content sentimentality could positively influence the perception of argument quality which lead to information adoption behavior. Finally, the importance of information relevancy was also highlighted in this study, which reveals some appropriate features that can be utilized by e-Commerce practitioners to better refine their information search criteria in the online review platforms