Introduction of the opinions of tourist services based on sentiment

Gomez, Alexis; Bano, Nausheen; Martinez, C. Carlos Eduardo; Gómez, Gustavo Adolfo Álvarez; Culque, T. Walter V.; Bustamante-Sánchez, Natalia Soledad

doi:10.23919/cisti.2018.8399376

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…In another study, Xiang et al (2015) also used a lexicon-based method to analyze hotel customer satisfaction sentiment. In addition, the SentiStrength lexicon has been used in tourism studies such as Gomez et al (2018) that used it to process opinions of tourist services. Likewise, Ramanathan and Meyyappan (2019) used it to obtain feedback about Oman’s tourism.…”

Section: Methodsmentioning

confidence: 99%

Topic and sentiment analysis of crisis communications about the COVID-19 pandemic in Twitter’s tourism hashtags

Carvache‐Franco

et al. 2022

Tourism and Hospitality Research

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The purpose of this paper was to assess Twitter as a means of communication during tourism crises with the following objectives: (a) identify the topics that are discussed, (b) establish the text sentiment, and (c) determine the differences in gender regarding the topics under discussion and the text sentiment. The data were collected from Twitter between March and April 2020. Using big data software, this study extracted 123,868 tweets globally in different languages through the Twitter API of popular tourism hashtags. Two techniques were applied: word association and sentiment analysis. The results show that the communication made through Tweets has the characteristics of a crisis communication related to the effects of the COVID-19 pandemic in the tourism industry. The theoretical contribution of the research is that Twitter in social media is an effective means of communication during pandemic crises and contributes to reducing negative perceptions and adverse effects of the tourism crises in companies and destinations. The practical contribution of the research is that Twitter can be used as a means of communication helping the communication strategies of companies and organizations.

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Section: Methodsmentioning

confidence: 99%

Topic and sentiment analysis of crisis communications about the COVID-19 pandemic in Twitter’s tourism hashtags

Carvache‐Franco

et al. 2022

Tourism and Hospitality Research

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“…The source of information for analyzing the polarity of emotions in the text were hotel services reviews in Trip Advisor, in the format shown below as an example (Gomez et al, 2018): <Overall Rating>3.5 <Avg. Price>$172 <URL>http://www.tripadvisor.com/ShowUserReviews-g60878-d72586-r23256277-Best_Western_Executive_Inn-Seattle_Washington.html <Author>ardarvin <Content>Deceptive Staff Deceptive front desk staff, claiming you cannot park on the street between 10pm and 4am ... in order to try and get you to pay the $15 parking fee with them.…”

Section: Methodsmentioning

confidence: 99%

Introduction to the Analysis of Tourism Services through Feelings Mining

Gómez

Lozada

Martínez

et al. 2018

Journal of Information Systems Engineering & Management

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One of the sources of information to detect the level of customer satisfaction is the set of reviews they leave on websites such as Trip Advisor. A problem arises when there is a large number of reviews and administrators have difficulty identifying those that reflect the client's feelings from the reviews. For this study, some applications generated in the Python programming language were used in order to assign an excellent and bad criterion by adding a feeling textually. The Sentistrength classifier was also used, which analyzes text to return a value of positive or negative sentiment; these two contexts were used mathematically and statistically to obtain referential data based on an analysis of the ROC curve with values of true positives and true negatives as well as false positives and false negatives.

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Utilizing Google Translated Reviews from Google Maps in Sentiment Analysis for Phuket Tourist Attractions

Mathayomchan

Sripanidkulchai

2019

2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE)

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Introduction of the opinions of tourist services based on sentiment

Cited by 3 publications

References 4 publications

Topic and sentiment analysis of crisis communications about the COVID-19 pandemic in Twitter’s tourism hashtags

Topic and sentiment analysis of crisis communications about the COVID-19 pandemic in Twitter’s tourism hashtags

Introduction to the Analysis of Tourism Services through Feelings Mining

Utilizing Google Translated Reviews from Google Maps in Sentiment Analysis for Phuket Tourist Attractions

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