scholarly journals Research on user generated content in Q&A system and online comments based on text mining

2022 ◽  
Author(s):  
Yahui Chen ◽  
Dongsheng Liu ◽  
Yanni Liu ◽  
Yiming Zheng ◽  
Bing Wang ◽  
...  
Keyword(s):  
Text Mining ◽  
User Generated Content ◽  
Online Comments

Determination of Guest Satisfaction by Text Mining

2022 ◽  
pp. 247-269
Author(s):  
Ozan Çatir
Keyword(s):  
Text Mining ◽  
Association Analysis ◽  
Hotel Management ◽  
Mining Method ◽  
Online Comments ◽  
Guest Satisfaction

The satisfaction of guests is of paramount importance to ensure the continuity and profitability of hotels. This study aims to determine guests' satisfaction with hotels by analyzing the online comments of guests. The text mining method has been utilized in this study. 58,193 Turkish comments about 5-star hotels in Turkey have been examined. These comments have been subjected to frequency and association analysis by models with Rapid Miner program. It may be stated that the guests are satisfied with 5-star hotel management in Turkey, and they are also satisfied with hotels in general and the services provided by hotels.

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.

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 On the Processing and Analysis of Microtexts: From Normalization to Semantics

Proceedings ◽  
2018 ◽  
Vol 2 (18) ◽  
pp. 1170
Author(s):  
Yerai Doval ◽  
David Vilares
Keyword(s):  
Text Mining ◽  
Business Intelligence ◽  
Health Surveillance ◽  
User Generated Content ◽  
Political Analysis ◽  
High Level ◽  
Source Of Information

User-generated content published on microblogging social platforms constitutes an invaluable source of information for diverse purposes: health surveillance, business intelligence, political analysis, etc. We present an overview of our work on the field of microtext processing covering the entire pipeline: from input preprocessing to high-level text mining applications.

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 Text mining-based analysis of online comments for skincare e-commerce

2021 ◽  
Vol 2010 (1) ◽  
pp. 012008
Author(s):  
Wen Hong ◽  
Yiping Wu ◽  
Shangze Li ◽  
Ying Wu ◽  
Zehai Zhou ◽  
...  
Keyword(s):  
Text Mining ◽  
Online Comments

scholarly journals Beyond Evil and Good in Online Gaming. An Analysis of Violence in ‘Overwatch’ Between Demonization and Proactive Values

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Enrico Gandolfi ◽  
Francesca Antonacci
Keyword(s):  
Content Analysis ◽  
Media Studies ◽  
Video Game ◽  
User Generated Content ◽  
Negative Attitudes ◽  
Online Comments ◽  
Key Concepts ◽  
Explicit Content ◽  
Educational Sciences ◽  
Media Environments

Many studies have addressed and explored the effects of video games with an emphasis on violence and aggressive behaviors. This article’s aim is to go beyond the simplistic difference between negative outcomes and their absence by suggesting the concept of “meaningful violence.” For exploring possible instances of such a phenomenon, a content analysis (Gee, 2012) of online materials (online comments, user-generated content) from leading gaming media environments (Reddit, YouTube) was directed targeting the popular video game Overwatch. The theoretical framework adopted drawn its cornerstones from Educational Sciences, Philosophy, and Media Studies, spanning key concepts such as “symbolic imaginary” (Durand, 1999, Wunenburger, 1995) and phenomenological-hermeneutic analysis (Gadamer, 2004). Results point to an alternative overview of gaming violence, which puts in-game aggressiveness and sacrifice in a new light beyond counter-posed viewpoints. Implications are noteworthy for both researchers and practitioners, who can harness positive and proactive processes behind apparently negative attitudes and superficial measurements of explicit content and disruptive actions.

scholarly journals A Three-Stage method for Data Text Mining: Using UGC in Business Intelligence Analysis

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 519 ◽  
Author(s):  
Saura ◽  
Bennett
Keyword(s):  
Text Mining ◽  
Business Intelligence ◽  
Latent Dirichlet Allocation ◽  
User Generated Content ◽  
Business Strategies ◽  
Intelligence Analysis ◽  
Digital Platforms ◽  
Digital Environments ◽  
Marketing Intelligence ◽  
Three Stages

The global development of the Internet, which has enabled the analysis of large amounts of data and the services linked to their use, has led companies to modify their business strategies in search of new ways to increase marketing productivity and profitability. Many strategies are based on business intelligence (BI) and marketing intelligence (MI) that make it possible to extract profitable knowledge and insights from large amounts of data generated by company customers in digital environments. In this context, the present study proposes a three-step research methodology based on data text mining (DTM). In further research, this methodology can be used for business intelligence analysis (BIA) strategies to analyze user generated content (UGC) in social networks and on digital platforms. The proposed methodology unfolds in the following three stages. First, a Latent Dirichlet Allocation (LDA) model that determines the database topic is used. Second, a sentiment analysis (SA) is proposed. This SA is applied to the LDA results to divide the topics identified in the sample into three sentiments. Thirdly, textual analysis (TA) with data text mining techniques is applied on the topics in each sentiment. The proposed methodology offers important advances in data text mining in terms of accuracy, reliability and insight generation for both researchers and practitioners seeking to improve the BIA processes in business and other sectors.

Sign in / Sign up

Export Citation Format

Share Document