The development of IoT technologies and the massive admiration and acceptance of social media tools and applications, new doors of opportunity have been opened for using data analytics in gaining meaningful insights from unstructured information. The application of opinion mining and sentiment analysis (OMSA) in the era of big data have been used a useful way in categorize the opinion into different sentiment and in general evaluating the mood of the public. Moreover, different techniques of OMSA have been developed over the years in different datasets and applied to various experimental settings. In this regard, this study presents a comprehensive systematic literature review, aims to discuss both technical aspect of OMSA (techniques, types) and non-technical aspect in the form of application areas are discussed. Furthermore, the study also highlighted both technical aspect of OMSA in the form of challenges in the development of its technique and non-technical challenges mainly based on its application. These challenges are presented as a future direction for research.

The purpose of this paper is to examine the effects of the Big Five Model (BFM), the urge to purchase (UP) and urgency (UR) on impulse purchase (IP) in Facebook commerce (F-commerce), with the F-commerce purchase as control variable. It also investigates the influence of BFM and UR on UP and the effects of BFM on UR. The survey instrument was rigorously validated via content validity index by expert panel, Q-sort procedure for construct validity by practitioners in pre-test, followed by evaluation of construct reliability in the pilot test. Data gathered from 808 usable questionnaires were analyzed using SmartPLS 3. The study showed that BFM, UP, UR and F-commerce purchase are significant predictors of the F-commerce IP. UP is influenced by BFM and UR. BFM has a significant positive relationship with UR. F-commerce experience has insignificant moderating effect. This study provides some useful practical implications for the F-commerce administrators, advertisers, dealers and promoters. Existing studies focus on the antecedents of IP in conventional stores and online businesses; however, IP in F-commerce has been largely overlooked. The study investigates the impacts of personality traits on IP and its effects on UR and UP. The mediating effects of UR and UP were also examined. The study is able to predict 64.4, 68.0 and 49.0 percent variance in IP, UP and UR, respectively.

This study explores tweets from Oct 2008 to Oct 2018 related to halal tourism. The tweets were extracted from twitter and underwent various cleaning processes. A total of 33,880 tweets were used for analysis. Analysis intended to (1) identify the topics users tweet about regarding halal tourism, and (2) analyze the emotion-based sentiment of the tweets. To identify and analyze the topics, the study used a word list, concordance graphs, semantic network analysis, and topic-modeling approaches. The NRC emotion lexicon was used to examine the sentiment of the tweets. The analysis illustrated that the word “halal” occurred in the highest number of tweets and was primarily associated with the words “food” and “hotel”. It was also observed that non-Muslim countries such as Japan and Thailand appear to be popular as halal tourist destinations. Sentiment analysis found that there were more positive than negative sentiments among the tweets. The findings have shown that halal tourism is a global market and not only restricted to Muslim countries. Thus, industry players should take the opportunity to use social media to their advantage to promote their halal tourism packages as it is an effective method of communication in this decade.

Although online product recommendation (OPR) is important in e-commerce transactions, there is a little understanding about differential impact of different recommendations. This study aims to examine the distinct effects of system-generated recommendation (SGR) and consumer-generated recommendation (CGR) on affective and social-psychological beliefs of OPR evaluation and to assess how they mediate the impact of OPR usage on purchase intentions. Results of a cross-sectional survey with 482 Amazon consumers showed that users of CGR express significantly higher trusting beliefs than users of SGR, while users of SGR elicit greater perceived enjoyment than users of CGR, resulting in mediating effect on purchase intentions. Moreover, CGRs were found to elicit greater trusting beliefs for experience products, while SGRs were found to unfold greater perceived enjoyment for search products.