An Empirical Analysis of Learners’ Experience on Select MOOCs Platforms with Reference to Users Reviews and Ratings

Massive open online courses (MOOCs) is created greater prominence as a modern learning system mainly due to the advanced progress made in the area of Learning and Teaching Technology and. Covid pandemic also had open opportunities for Online Learning Platforms. Present study has focused on learners’ experience with various MOOCs platforms through online reviews and ratings, which were collected from Google play store and appbot application. Seven MOOCs platforms namely Coursera, edX, Udemy, Swayam, LinkedIn , Khan Academy and Upgrad are reviewed in this paper. The main objective is to compare the select MOOCs platforms in the area of users’ experience. Total number of reviews and rating has been taken for the study is 63, 652. The time frame of sample data was taken for last one year that is from 5th April, 2020 to 5th April, 2021. Sentiment analysis and chi-square test is applied to analyze the difference among the different MOOCs platforms. The major outcomes were the reviews and ratings of different platform found with very good uses experience.

Sentiment Analysis using Optimized Feature Sets in Different Facebook/Twitter Dataset Domains using Big Data

Now days, in many real life applications, the sentiment analysis plays very vital role for automatic prediction of human being activities especially on online social networks (OSNs). Therefore since from last decade, the research on opinion mining and sentiment analysis is growing with increasing volume of online reviews available over the social media networks like Facebook OSNs. Sentiment analysis falls under the data mining domain research problem. Sentiment analysis is kind of text mining process used to determine the subjective attitude like sentiment from the written texts and hence becoming the main research interest in domain of natural language processing and data mining. The main task in sentiment analysis is classifying human sentiment with objective of classifying the sentiment or emotion of end users for their specific text on OSNs. There are number of research methods designed already for sentiment analysis. There are many factors like accuracy, efficiency, speed etc. used to evaluate the effectiveness of sentiment analysis methods. The MapReduce framework under the domain of big-data is used to minimize the speed of execution and efficiency recently with many data mining methods. The sentiment analysis for Facebook OSNs messages is very challenging tasks as compared to other sentiment analysis because of misspellings and slang words presence in twitter dataset. In this paper, different solutions recently presented are discussed in detail. Then proposed the new approach for sentiment analysis based on hybrid features extraction methods and multi-class Support Vector Machine (SVM). These algorithms are designed using the Big-data techniques to optimize the performance of sentiment analysis

Impact of the length of stay at hotels on online reviews

Purpose The length of stay (LoS) is of major importance from the perspective of the management of tourist destinations. As tourists heavily rely on the online reviews of other travelers as a primary information source, this study aims to empirically examine how the LoS can influence the online reviews for hotels, with special emphasis on the textual review content. Design/methodology/approach This study analyzes online review data collected from Booking.com by using the Linguistic Inquiry and Word Count program to operationalize review depth, analytical thinking and the authenticity reflected in customer reviews. Based on the analyzed data, this study used a series of regression analyses to understand the impacts of the LoS on online reviews. Findings The author’s analysis found that a longer stay at a hotel causes consumers to be more likely to post online reviews that not only include a numerical rating as well as written content but also lengthier and more detailed descriptions of their hotel experiences. Further analysis found that the LoS at hotels causes systematic differences in the linguistic attributes of the review content. Specifically, consumers who stay longer tend to write reviews with more analytical information, resulting in consumers perceiving the online reviews as more authentic. Research limitations/implications Although the LoS has been considered a significant issue in tourism, studies examining the impact of different lengths of stay on consumers’ post-purchase behaviors are limited. In this light, the author’s findings demonstrate how the LoS can change the linguistic attributes of online reviews. It expands the body of knowledge of the LoS in tourism. Originality/value This study represents the first attempt to empirically examine and reveal how the different length of stay at a hotel systemically influences consumer review-posting behaviors.

AN ONLINE REVIEWS INFORMATION FUSION METHOD AND ITS APPLICATION TO PUBLIC PROPERTY SERVICE QUALITY EVALUATION

With the rapid development of computer networking technology, people pay more and more attention to the role of online reviews in management decision making. The existing methods of online reviews fusion are limited to rational decision-making behavior, which does not accord with the characteristics of evaluators’ behavior characteristics in the real environment. In order to solve the online reviews fusion problem based on bounded rational behavior which is closer to the reality of property service quality evaluation, the multi-index and multi-scale (MIMS) method is extended into the generalized form, the online reviews are quantified by using the adverb structure scaling method, and an online reviews fusion method based on the improved TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) model is proposed. The feasibility and effectiveness of the proposed method are verified by an example analysis of property service quality evaluation. The research results are as follows: the adverb structure scaling method is suitable for a large number of online reviews processing, the proposed method improves the efficiency of online reviews information fusion, and it is feasible and effective to evaluate property service quality based on the bounded rationality of evaluator’s behavior.

The Impact of Hotel Customer Experience on Customer Satisfaction through Online Reviews

With the growing popularity of the internet, customers can easily share their experiences and information in online reviews. Consumers recognize online reviews as a useful source of information prior to consumption, and many online reviews influence consumer purchasing decisions. Understanding the customer experience in online reviews is thus necessary to maintain customer satisfaction and repurchase intention for the sustainable development of the hotel business. This study assessed the fundamental selection attributes of customers from online reviews reflecting the hotel customer experience, and investigated their association with customer satisfaction. A total of 8229 reviews were collected from Google travel websites from December 2019 to July 2021. Text mining and semantic network analysis were adopted for big data analysis. Factor and regression analyses were then used for quantitative analysis. Based on linear regression analysis, the Service and Dining factors significantly affected customer satisfaction. Service is a critical selection attribute for customers, and the provision of more particular services is necessary, especially after COVID-19. These results indicate that understanding online reviews can provide theoretical and practical implications for developing sustainable strategies for the hotel industry.