Mining Online User-Generated Content: Using Sentiment Analysis Technique to Study Hotel Service Quality

Duan, Wenjing; Cao, Qing; Yu, Yang; Levy, Stuart E.

doi:10.1109/hicss.2013.400

Cited by 94 publications

(57 citation statements)

References 50 publications

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“…They find a positive asymmetrical relationship between positive affective content and the conversion rates on the website. Duan, Cao, Yu, and Levy [15] use the sentiment analysis technique to mine 70,103 online user reviews posted in various online venues from 1999 to 2011 for 86 hotels in Washington, DC. Sentiment analysis helps them decompose user reviews into five dimensions to measure hotel service quality, and the sentiment analysis results show a high level of accuracy in capturing and measuring service quality dimensions, compared with existing text mining studies.…”

Section: Sentiment Analysismentioning

confidence: 99%

A novel social media competitive analytics framework with sentiment benchmarks

Yan

et al. 2015

Information & Management

195

107

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Section: Sentiment Analysismentioning

confidence: 99%

A novel social media competitive analytics framework with sentiment benchmarks

Yan

et al. 2015

Information & Management

195

107

View full text Add to dashboard Cite

“…Expressing customers thought about a particular product or Service on web has become a major tool. The reviews which are used is classify by Naive Bayes method, Support vector machine, Laplace smoothing and Semantic orientation, Term Frequency and Inverse Document Frequency method in sentiment analysis techniques [11].…”

Section: Literature Surveymentioning

confidence: 99%

Sentiment Analysis for Customer Opinion on Hotel Using Machine Learning Techniques

Balande*¹,

Deshmukh²

2019

IJITEE

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Opinions from others play a significant part to take our own decision, The people’s opinions, attitudes and emotions are a computational study toward an entity is called as Sentiment Analysis (SA) or Opinion Mining (OM). In today's world, everything like business, organization and even individuals wants to know opinion from public or customers about their presentation, products and about their services which will give clear idea about their product, portfolio in the market and if these services is not up to the mark how their services they improve, so that their business will perform better. To give output as positive, negative or neutral and find the difference of a specified user text or data from the dataset is the main task of the sentiment or opinion analysis. The opinions, sentiments and subjectivity of text are computational treatment in text mining with Sentiment Analysis (SA). With the help of sentiment analysis this paper describe the machine learning classification techniques for hotel reviews for which dataset obtained from Trip advisor hotel reviews website. System got 99.07 % accuracy for MAXENT Classifier with Train size and Test size 80% and 20% respectively.

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“…5,6 In addition, managing these customer reviews has been proven as an efficient approach to evaluating various dimensions of service quality. [7][8][9] Statistical process control (SPC) is an efficient tool for monitoring various quality characteristics and has been widely used in many cases, including continuous and categorical processes. 10,11 With its extraordinary performance in process monitoring, SPC can be applied to online product reviews for quickly detecting the hidden evolution in customer opinions and monitoring the quality of products and relevant services in an after-sales stage.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of user‐generated reviews via a sequential reverse joint sentiment‐topic model

Liang

Wang

2019

Quality & Reliability Eng

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User‐generated reviews can serve as an efficient tool for evaluating the customer‐perceived quality of online products and services. This article proposes a joint control chart for monitoring the quantitative evolution of document‐level topics and sentiments in online customer reviews. A sequential model is constructed to convert the temporally correlated document collections to topic and sentiment distributions, which are subsequently used to monitor the topics that users are concerned about and the topic‐specific opinions in an ongoing product and service process. Simulation studies on various data scenarios demonstrate the superior performance of the proposed control chart in terms of both detecting shifts and identifying truly out‐of‐control terms.

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Mining Online User-Generated Content: Using Sentiment Analysis Technique to Study Hotel Service Quality

Cited by 94 publications

References 50 publications

A novel social media competitive analytics framework with sentiment benchmarks

A novel social media competitive analytics framework with sentiment benchmarks

Sentiment Analysis for Customer Opinion on Hotel Using Machine Learning Techniques

Monitoring of user‐generated reviews via a sequential reverse joint sentiment‐topic model

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