Sentiment Analysis of Customer Satisfaction on Transportation Network Company Using Naive Bayes Classifier

Sari, Eka Yulia; Wierfi, Akrilvalerat Deainert; Setyanto, Arief

doi:10.1109/cenim48368.2019.8973262

Cited by 17 publications

(10 citation statements)

References 13 publications

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“…The methodology is the application of SA that produces quantitative and qualitative results. The use of mixed methodology through SA is related to this new focus on feelings of consumers as a new way to calculated CS (Sari et al, 2019). For the quantitative part, the tweets of the two seasons were combined for a comparison of the opinion and satisfaction of the service of Trenitalia between two years.…”

Section: Methodsmentioning

confidence: 99%

“…However, the study on CS from the tourist perspective research is sparse; more research is needed on CS from the tourist perspective (Ravishankar & Christopher, 2020). CS can be related to feelings of enjoyment, acceptance, ease, and happiness (Sari et al, 2019). Starting from the feelings of the customers, as a basis of CS in the field of tourism, this work will focus on a methodology useful to detect the emotions of tourists, namely SA.…”

Section: Joint Decisionmentioning

confidence: 99%

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SentiSfaction: New cultural way to measure tourist COVID-19 mobility in Italy

Papapicco

2023

MEDITERR J SOC BEH RES

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From a psycho-linguistic and marketing perspective, the research fits into the evaluation of in the context of tourism and, in particular, tourism mobility, targeting one of the leading Italian rail transport companies, namely Trenitalia. This study, conducted on tweets, aims to examine how talks about the transport service offered by Trenitalia. A total of 674 tweets for the tourist season 2019 and 100 tweets for the tourist season 2020 were collected following the pre-COVID-19 and COVID-19 period. The methodology is the application of sentiment analysis (SA) that produces quantitative and qualitative results. For the quantitative part, the sentiment was calculated first automatically via the Sentistrength software, then an extraction of the frequencies and calculation of the dependence (Chi-square statistic and t-test) between year and polarity was conducted with R, statistical software. The results show that SA is a good methodology of analysis of the online reputation and customer satisfaction of a company that deals with tourism, also in the difference between pre-COVID-19 and COVID-19 period.

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Section: Methodsmentioning

confidence: 99%

Section: Joint Decisionmentioning

confidence: 99%

SentiSfaction: New cultural way to measure tourist COVID-19 mobility in Italy

Papapicco

2023

MEDITERR J SOC BEH RES

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“…The move restriction has been proved by many countries can reduce the number of positive and dead cases [4], [18]- [23]. The comparison of Multiple Linear Regression (MLR), Support Vector Regression (SVR), Random Forest Regressor, and Decision Tree Regressor are conducted to know the best regression method [24]- [26]. In contrast, the classification methods are implemented to determine the movement restriction status.…”

Section: Related Workmentioning

confidence: 99%

A Covid-19 Positive Case Prediction and People Movement Restriction Classification

Agastya¹

2022

IJACSA

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The world experienced a pandemic that changed people's daily life due to Coronavirus Disease 2019 (covid-19). In Jakarta, the covid-19 cases were discovered on March 18, 2020, and the case increased uncontrollably until the government conducted a movement restriction called pembatasan sosial berskala besar (PSBB). The effectivity of movement restriction was not evaluated in detail. Therefore, we investigated the covid-19 cases in the PSBB period to understand the contribution of movement restriction. Moreover, a prediction model is proposed to computerize the decision of movement restriction. The models are divided into regression and classification models. The regression model is developed to forecast the number of infected cases. At the same time, the classification model is used to identify the best movement restriction type. We utilize data transformation named Principal Component Analysis (PCA) to reduce the number of features. In our case, the best regression method is Multiple Linear Regression (MLP). Then, the best classification method is the Support Vector Machine (SVM). The MLP results are 148.38, 37036.37, and 0.250336 for Mean Absolute Error (MAE), Mean Square Error (MSE), and R 2 , respectively. In contrast, the SVM achieved an accuracy of 84.81%. Moreover, the prediction system on the website were successfully deployed.

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“…Chaniotakis et al (2016) have provided a comprehensive review of the directions that transportation-related Social Media research is positioned. In short, the directions that the literature takes are either the use of Social Media for modeling and forecasting purposes, including an aspect of the use of Social Media data for OD Estimation (Liao et al, 2021;Osorio-Arjona and García-Palomares, 2019), Attraction Models (Lee et al, 2019;Yang et al, 2018;Hu and Jin, 2018), activity modelling (Cui et al, 2018;Chaniotakis et al, 2017;Hasan and Ukkusuri, 2018;Lee et al, 2016), extraction of mobility-related and spatial characteristics (Ebrahimpour et al, 2020;Hu et al, 2020;Kim et al, 2018;Yao et al, 2018;Yang et al, 2019) transportation-related sentiment analysis (Rahman et al, 2021;Bakalos et al, 2020;Sari et al, 2019;Ali et al, 2018Ali et al, , 2019, prediction and event detection (Chaturvedi et al, 2021;Yao and Qian, 2021;Alomari et al, 2019Alomari et al, , 2021Zulfikar et al, 2019;Zhang et al, 2018;Xu et al, 2018;Pereira et al, 2015), and accessibility analysis with the complementary use of Twitter data (Kim and Lee, 2021;Qian et al, 2020;Moyano et al, 2018). On another perspective, social media have also been used mainly from transport providers, for the direct communication that their platform allow with the end users (National Academies of Sciences, Engineering, and Medicine, 2021).…”

Section: Introductionmentioning

confidence: 99%