eWOW of Guests Regarding Their Hotel Experience

2020 ◽  
pp. 295-308
Author(s):  
Zelia Breda ◽  
Rui Costa ◽  
Gorete Dinis ◽  
Amandine Angie Martins
Keyword(s):  
Sentiment Analysis ◽  
Online Reviews ◽  
User Generated Content ◽  
Sources Of Information ◽  
Value For Money ◽  
Quality Of Sleep ◽  
Online Comments ◽  
Analytical Response ◽  
Source Of Information

Online comments are increasingly mentioned as an important source of information, simplifying consumers' buying decisions. Online user-generated content has become one of the main sources of information for tourists, who themselves become creators of their own online content. This chapter focuses on sentiment analysis of comments made on TripAdvisor regarding one resort located in the Algarve region, in Portugal. The resort has good reviews, which means that the eWOM is positive. The highest scores relate to the resort's cleanliness, location and quality of sleep, and those that were less relevant were the value for money, the rooms and the service. The most dominant emotion is joy, followed by an analytical response. Negative emotions, such as sadness and anger, were not found very often in the online reviews. These results could be explained by the quality of the service, the kindness of the staff, the facilities for children, the entertainment, and the location, attributes that were often highlighted in the comments.

Effect of Temporal Arrangement on Multimedia Learning

2018 ◽  
Author(s):  
Victoria Chen
Keyword(s):  
Prior Knowledge ◽  
Cognitive Processing ◽  
Teaching And Learning ◽  
Multimedia Learning ◽  
Primary Source ◽  
Sources Of Information ◽  
Multimedia Teaching ◽  
Text Information ◽  
Source Of Information

The purpose of this study is to examine whether Multimedia learning theory (Mayer, 1997; Schnotz & Kürschner, 2007) holds true when images are the primary source of information and text information is secondary. I will test how temporal arrangement of audio and image presentations affects quality of learning in this situation. I hypothesize that when audio is played before or after the image participants will require increased cognitive processing to mentally integrate the two sources of information resulting in deeper learning and transfer of learning. On the other hand when audio is played while the image is shown, I hypothesize that participants with high prior knowledge of the subject will score lower than participants with low prior knowledge, because prior knowledge will interfere with knowledge from the two sources causing a redundancy effect. This experiment will lead to greater understanding of multimedia teaching and learning in classrooms as well as how it affects deeper learning.

scholarly journals Epistemological and ethical problems of information era and citing of "Wikipedia"

2015 ◽  
Vol 58 (2) ◽  
pp. 107-121
Author(s):  
Milicа Vucurovic
Keyword(s):  
Philosophy Of Science ◽  
Academic Community ◽  
Sources Of Information ◽  
Quality Of Information ◽  
Ethical Problems ◽  
Scientific Responsibility ◽  
Utilitarian Approach ◽  
Source Of Information

Wikipedia as an internet phenomenon enjoys a great popularity, even in the academic community, thus rising a legitimate question, does it present a valid source of information, but even more important question is: What are the implications of the epistemological and ethical sense behind the conformism of the accelerating and shallow search due to inflation of information, where Wikipedia is just one of the representatives quick sources of information which most often is applied. From the ethical side, Wikipedia's politics is implicitly committed to utilitarianism, and institutional morale and professions are considered redundant, while the hypothesis that we have tried to defend is the opposite and based on models that support the scientific responsibility of the authors and publishers. But even in utilitarian approach, classical methods of philosophy of science more effective, partly because in the era of information epistemological justification of the reliability of the evidence and the chain of references to new data and milions of publications is much more important than the quality of information as such.

scholarly journals Mining Express Service Innovation Opportunity from Online Reviews

2021 ◽  
Vol 33 (6) ◽  
pp. 0-0
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Sentiment Analysis ◽  
Service Innovation ◽  
Online Reviews ◽  
User Generated Content ◽  
Customer Demand ◽  
Market Competitiveness ◽  
Service Attribute ◽  
Express Delivery

In order to further meet the diversified needs of customers and enhance market competitiveness, it is necessary for express delivery enterprises to carry out service innovation. From the perspective of customer demand, we propose a framework for mining service innovation opportunities. This framework uses text mining to analyze user generated content and tries to provide a scientific service innovation scheme for express enterprises. Firstly, we crawl online reviews of express companies and use LDA model to identify service attributes. Secondly, customer satisfaction is calculated by sentiment analysis, and simultaneously, the importance of each service attribute is calculated. Finally, we carry out an opportunity algorithm with the results of text mining to quantify the innovation opportunities of service attributes. The results show that the framework can effectively identify service innovation opportunities from the perspective of customer demand. This study provides a new way to explore the direction of service innovation from the perspective of customer demand.

scholarly journals Learning from Online Voices: A Mixed Methods Approach to Explore Patient Online Reviews of Hospital Care in Portugal

2021 ◽  
Author(s):  
Carla Marisa Ferreira Gomes ◽  
Marlene Paula Castro Amorim ◽  
Mário Jorge Ferreira Rodrigues
Keyword(s):  
Hospital Care ◽  
Information Source ◽  
Healthcare Services ◽  
Online Reviews ◽  
Online Comments ◽  
Structured Analysis ◽  
Important Quality ◽  
Rich Information ◽  
The Right

Online patient reviews can offer a rich information source to users of healthcare services, as well as for hospital management and quality monitoring. Whereas in recent years the volume of online patient reviews has been consistently growing, organizations still lack standardized approaches and tools to allow for the systematic monitoring of users’ online comments. Therefore, managers are lagging in the ability to make use of such data from patients’ voices for improving the quality of the services provided. If organizations fail to develop the right capabilities to consider users’ online reviews and feedback, they risk not only to miss important quality failure alerts, as wells as to frustrate their customers’ expectations for service and attention. In this chapter, we present a qualitative analysis of patients’ reviews for healthcare services in Portugal, building on a sample of data extracted from Google for the year of 2019. The chapter reports the major quality management themes addressed by hospital users in their online expressions and offers some guidelines to support a structured analysis and visualization of results from online users’ word of mouth data.

scholarly journals Mining Express Service Innovation Opportunity From Online Reviews

2021 ◽  
Vol 33 (6) ◽  
pp. 1-15
Author(s):  
Ning Zhang ◽  
Rui Zhang ◽  
Zhiliang Pang ◽  
Xue Liu ◽  
Wenfei Zhao
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Sentiment Analysis ◽  
Service Innovation ◽  
Online Reviews ◽  
User Generated Content ◽  
Customer Demand ◽  
Market Competitiveness ◽  
Service Attribute ◽  
Express Delivery

In order to further meet the diversified needs of customers and enhance market competitiveness, it is necessary for express delivery enterprises to carry out service innovation. From the perspective of customer demand, we propose a framework for mining service innovation opportunities. This framework uses text mining to analyze user generated content and tries to provide a scientific service innovation scheme for express enterprises. Firstly, we crawl online reviews of express companies and use LDA model to identify service attributes. Secondly, customer satisfaction is calculated by sentiment analysis, and simultaneously, the importance of each service attribute is calculated. Finally, we carry out an opportunity algorithm with the results of text mining to quantify the innovation opportunities of service attributes. The results show that the framework can effectively identify service innovation opportunities from the perspective of customer demand. This study provides a new way to explore the direction of service innovation from the perspective of customer demand.

scholarly journals Exploring Consumer Emotions in Pre-Pandemic and Pandemic Times. A Sentiment Analysis of Perceptions in the Fine-Dining Restaurant Industry in Bucharest, Romania

2021 ◽  
Vol 18 (24) ◽  
pp. 13300
Author(s):  
Jacqueline-Nathalie Harba ◽  
Gabriela Tigu ◽  
Adriana AnaMaria Davidescu
Keyword(s):  
Content Analysis ◽  
Sentiment Analysis ◽  
Online Reviews ◽  
Restaurant Industry ◽  
Capital City ◽  
Choice Making ◽  
Consumer Emotions ◽  
Cognitive Choice ◽  
Media Sentiment

This research paper aims to analyse how consumer emotions have evolved during the pandemic period in comparison with the pre-pandemic period in relation to restaurant demand in the Romanian fine-dining industry and uses valuable information based on social-media sentiment analysis and content analysis. Focusing on theories of consumer behaviour, the study aims to emphasize how, under the influence of an epidemic crisis caused by an infectious disease, individual behaviour adapts to the “new normal”, embracing a series of changes in the preferences, attitudes, and cognitive choice-making processes. The article takes into account a comparative analysis of the consumer emotions between the pre-COVID-19 pandemic period (2010–2019) and the pandemic period (2020–present), based on the online reviews provided by customers for five fine-dining restaurants from Bucharest, the capital city of Romania: The Artist, Relais & Chateaux Le Bistrot Francais, Casa di David, Kaiamo, and L’Atelier. The research was based on two mining analyses—content analysis and sentiment analysis—and explored the emotional intent of words, with the data being collected from TripAdvisor through web-scrapping. The empirical results defined the fine-dining experience during the pandemic as being associated with the quality of the dishes and also with the quality of the service. The overall consumer sentiment in the direction of the restaurants analyzed is positive. The sentiment research found that throughout the epidemic, the consumers’ attitudes about restaurants deteriorated. In this sense, consumers seem to be less satisfied with the restaurants’ services than before the pandemic. This is another thing that the restaurants had difficulties in when adapting their operations for the pandemic.

Analytical review of approaches for tonality recognition of Russian text data

2020 ◽  
pp. 20-30
Author(s):  
Anastasiya Dvoynikova ◽  
Alexey Karpov
Keyword(s):  
Sentiment Analysis ◽  
Online Reviews ◽  
Russian Text ◽  
Software Systems ◽  
Text Data ◽  
World Languages ◽  
Analytical Review ◽  
Audio Data ◽  
Quality Of Products

Introduction: In recent years, sentiment analysis has found practical application in many areas, such as evaluating the quality of products and services based on customers’ online reviews, analyzing negative emotions in messages, forecasting stock markets or political situations based on news data. In this regard, a large number of systems and methods for Russian text sentiment analysis are being developed. Purpose: A detailed review of approaches, and comparative analysis of available databases in the field of Russian text sentimental analysis. Results: Our analytical review of the approaches to Russian text data sentiment analysis has shown that there are a large number of ways for preprocessing, vectorization and machine classification of the text data. Studying the available databases shows that the Russian text sentimental analysis is less developed than that for other major world languages. Studying the existing software systems for Russian text analysis reveals their low accuracy compared to English, which can be caused by the sophisticated structure of Russian. Discussion: In our further research, we plan to implement sentiment analysis of spoken speech using audio data. To do this, we will need to obtain a spelling transcription of speech for each speaker.

scholarly journals Airbnb or Hotel?

2020 ◽  
Vol 4 (2) ◽  
pp. 1-10
Author(s):  
Zhiyong Li ◽  
Honglin Chen ◽  
Xia Huang
Keyword(s):  
Information Technology ◽  
Social Media ◽  
Big Data ◽  
Online Reviews ◽  
Tourism Industry ◽  
Sources Of Information ◽  
Value For Money ◽  
Consumer Awareness ◽  
Positive Sentiment ◽  
Negative Sentiment

Advances in information technology have hugely influenced the tourism industry. Many tourists can generate and share their travel tips through social media, and people consult online reviews before making travel arrangements because they could access these sources of information easily. Either positive or negative reviews could increase consumer awareness of Airbnb. Using the approach of text mining and sentiment analysis, examining whether guests' emotions are positive or negative, this study investigates the attributes that influence Airbnb consumers' experiences compared with their previous hotel experiences by analysing big data of guests' online reviews. Findings reveal that the factors of guests' positive sentiment are the atmosphere, flexibility, special amenities, and humanized service; the factors of guests' negative sentiment are not value for money, have to clean the room before leaving, sharing amenities and space with strangers, disturbed by hosts' noisy recreational activities, and troubled by hosts' requesting good reviews.

Do Online Friends Bring Out the Best in Us? The Effect of Friend Contributions on Online Review Provision

2020 ◽  
Vol 31 (4) ◽  
pp. 1322-1336
Author(s):  
Zhihong Ke ◽  
De Liu ◽  
Daniel J. Brass
Keyword(s):  
Social Networking ◽  
Financial Incentives ◽  
Online Community ◽  
Online Reviews ◽  
Online Review ◽  
Consumer Decision Making ◽  
Online Platforms ◽  
Source Of Information ◽  
Willingness To Contribute

Online reviews are a crucial source of information for consumer decision making. Many businesses, companies, and platforms are interested in encouraging more consumers to review their products but are dubious about using financial incentives to buy online reviews. Our research describes a social way of growing online reviews. We show that cultivating an online community for reviewing by showing members reviews written by their online friends cannot only increasing their willingness to contribute but also the quality of the resulting reviews. The takeaways of this study include (1) unlike using financial rewards to incentivize review contribution, the studied approach can motivate review contributions without compromising the quality of reviews contributed; (2) the effect of exposing consumers to their friends’ reviews is comparable to that of Yelp’s weekly newsletters (thus this can be a powerful way of motivating consumer reviews); and (3) to effectively leverage friend reviews, online platforms should facilitate social networking among users and build an online community that recognizes and rewards members who make frequent, high-quality contributions to online reviews.

Sign in / Sign up

Export Citation Format

Share Document