A novel approach to analyze the sentiments of tweets related to TripAdvisor

Bhardwaj, Piyush; Gautam, Suruchi; Pahwa, Payal

doi:10.1080/02522667.2017.1417726

Cited by 16 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study shows that the model built with the DWkNN can provide new solutions and contributions compared to previous studies, as shown in Table 7. [13] Naïve Bayes 76.74 [27] Naïve Bayes Kernel Estimator 80.14 [30] kNN 86…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Decision Guidance for Solving Success Rate Political Campaign Using Distance Weighted kNN in Nassi-Shneiderman Framework

Nugroho¹,

Sumpeno²,

Fujisawa³

et al. 2021

IJIES

View full text Add to dashboard Cite

Campaigns on online social media are becoming intriguing because all parties can compete with each other. Various content can be created to support or defeat one another. Interaction is important to control the battlefield in the face of disinformation. The campaign team must be able to increase interaction and reporting it as a form of achievement. The reality is that the campaign team has not been able to provide a measurable report on the success rate of political campaign interaction on social media. This is because there is no standardization in measuring these interactions. This fact inspires to build a model for measuring campaign interaction on social media. This model will be able to help the campaign team. This study uses a dataset from a governor election campaign in Indonesia which is then tested on a presidential election campaign dataset in America. The model was built using machine learning with the k classification method Nearest Neighbor (kNN) and Distance Weight kNN (DWkNN). The research stages were arranged using the Nassi-Shneiderman Framework. This framework describes the stages and comparison of the use of the KNN and the DWkNN from the training to testing stages in an easy to understand manner. The kNN and DWkNN training stages showed excellent accuracy results of nearly 100%. Furthermore, at the testing stage using the crossvalidation method with a variety of fold 5 to 20 variations showed excellent results, namely 99.89% on the governor election dataset and a range of 98% on the American presidential election dataset. This classification model has been tested using several datasets from several candidate social media accounts. We also created a dataset campaign interaction. This study shows that the proposed model can outperform previous studies. So, that it provides novelty in the form of a campaign interaction guide model for the campaign team.

show abstract

Section: Resultsmentioning

confidence: 99%

“…The accuracy resulting from this classification is 83%. This study shows that data from Twitter can be classified to help measure customer satisfaction [27]. However, this research has not been able to show how the campaign plan and the measure of the success of the interaction.…”

Section: Classificationmentioning

confidence: 87%

A Decision Guidance for Solving Success Rate Political Campaign Using Distance Weighted kNN in Nassi-Shneiderman Framework

Nugroho¹,

Sumpeno²,

Fujisawa³

et al. 2021

IJIES

View full text Add to dashboard Cite

show abstract

“…Additionally, TripAdvisor checks the system to avoid false reviews (Filieri et al , 2015; Schuckert et al , 2015; O’connor, 2010). Sentiment analysis has been applied to the analysis of service quality and service failure through TripAdvisor reviews mainly of restaurants (Bhardwaj et al , 2018; Agüero-Torales et al , 2019; Wang et al , 2019; Zuheros et al , 2021) but also heritage sites and natural parks (Lee et al , 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing TripAdvisor reviews of wine tours: an approach based on text mining and sentiment analysis

Barbierato

Bernetti

Capecchi

2021

IJWBR

View full text Add to dashboard Cite

Purpose Wine packaged tours as a specific aspect of wine tourism have so far been neglected in research, for this reason, the purpose of this study is to study the key elements for the success of the wine tour in Tuscany (Italy), evaluating the points of strength and weakness. Design/methodology/approach The study combines approaches of text mining, sentiment analysis and natural language processing, drawing on data from the TripAdvisor platform, obtaining through an automatic procedure 9,616 reviews from 600 tours in the years 2010–2020. Findings The authors identified six elements of successful wine tours expressed by research subjects: tour guide; logistical aspects; the quality of the wine; the quality of the food; complementary tourist and recreational activities; the landscape and historic villages. The key strength associated with success was the integration of the leading wine product with food, landscape and historic villages, while the main criticisms were concerned with the organization and planning of the tour. Furthermore, the tour guide also plays a fundamental role in consumer satisfaction. Research limitations/implications The limitations of the method were linked to the origin of the data used. The main one is that TripAdvisor does not allow you to have social and personal information about the tourist who wrote the review; therefore, the methods are substantially complementary to the traditional survey through questionnaires. Practical implications The proposed model can be used both by professionals to improve the quality of their products and by policymakers to promote the territorial development of quality wine-growing areas. Social implications The proposed model can be useful for policymakers to promote the territorial development of quality wine-growing areas. Originality/value The methodology we tested is easily transferable to many countries and to the authors’ knowledge, for the first time attempts to combine multidimensional scaling, sentiment analysis and natural language processing approaches.

show abstract

“…Indian scholars P. Bhardwai, S. Gautam and P. Pahwa analyzed the emotions that Delhi, Bangalore, Mumbai and other metropolitan areas of India caused in tourists relying on the reviews of TripAdvisor. com visitors [24]. Chinese authors H. Zhang, J. Pu, C. Wang and H. Chen offer a model for analyzing restaurant reviews posted on TripAdvisor.…”

Section: International Tourism In the World And In The Baltic Regionmentioning

confidence: 99%

Tourism and the structure of attractiveness of the Baltic region metropolises

Рахманов

2019

Balt. Reg.

View full text Add to dashboard Cite

International tourism is playing an increasingly important part in the life of all the nine countries of the Baltic region. In this contribution, I analyse the statistical data for 2010—2017 regarding the numbers of arrivals of international tourists and international tourism revenues in the Baltic region. Regional metropolises, which include nine capitals and Saint Petersburg, have a pivotal role in the tourism space of the region. I propose a methodology for empirical research into the attractiveness of ten Baltic cities as perceived by international tourists. This methodology distinguishes three major components in the tourism industry of the Baltic metropolises: hotels, restaurants, and sights. I estimate the attractiveness of these tourism infrastructure components in each of the ten cities using special indicators. Based on the data obtained, I calculate the integrated indicator of city attractiveness. The empirical study shows that, in the Baltic region, international tourists appreciate the most the hotels of Berlin, Warsaw, and Copenhagen, the restaurants of Tallinn, Riga, and Copenhagen, and the sights of Berlin, Stockholm, and Saint Petersburg. The most attractive Baltic cities for international tourists are Berlin, Copenhagen, and Stockholm. Although the sights of Moscow and Saint Petersburg are competitive in the tourist space of the Baltic region, Moscow and Saint Petersburg hotels and restaurants are noticeably inferior to those in other countries of the region.

show abstract

A novel approach to analyze the sentiments of tweets related to TripAdvisor

Cited by 16 publications

References 13 publications

A Decision Guidance for Solving Success Rate Political Campaign Using Distance Weighted kNN in Nassi-Shneiderman Framework

A Decision Guidance for Solving Success Rate Political Campaign Using Distance Weighted kNN in Nassi-Shneiderman Framework

Analyzing TripAdvisor reviews of wine tours: an approach based on text mining and sentiment analysis

Tourism and the structure of attractiveness of the Baltic region metropolises

Contact Info

Product

Resources

About

A novel approach to analyze the sentiments of tweets related to TripAdvisor

Cited by 16 publications

References 13 publications

A Decision Guidance for Solving Success Rate Political Campaign Using Distance Weighted kNN in Nassi-Shneiderman Framework

A Decision Guidance for Solving Success Rate Political Campaign Using Distance Weighted kNN in Nassi-Shneiderman Framework

Analyzing TripAdvisor reviews of wine tours: an approach based on text mining and sentiment analysis

Tourism and the structure of attractiveness of the Baltic region met­ropolises

Contact Info

Product

Resources

About

Tourism and the structure of attractiveness of the Baltic region metropolises