Twitter Social Media Sentiment Analysis in Tourist Destinations Using Algorithms Naive Bayes Classifier

Hermanto, Dedy; Ziaurrahman, M; Bianto, Mufti Ari; Setyanto, Arief

doi:10.1088/1742-6596/1140/1/012037

Cited by 15 publications

(7 citation statements)

References 1 publication

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“…Therefore, we need the dataset points to be as far off from the hyperplane as possible while being on the correct side. In this way, when new testing data is added, whatever side of the hyperplane it is situated will choose the class that we assign to it [5].…”

Section: Naive Bayesmentioning

confidence: 99%

“…Many researchers have used sentiment analysis in different aspects of life. Research by Hermanto et al [5] investigated the tourism sector in Indonesia, which is influenced by the innovation that affects the freedom to access data. A tweet as an online media that reflects tourists' emotions has the chance to reveal information about the traveler locations that they will or already have visited, such as tourists' experience, tourists' feedback on a place of interest, and other vacation destinations.…”

Section: Introductionmentioning

confidence: 99%

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Course Evaluation Based on Deep Learning and SSA Hyperparameters Optimization

Pramanik¹,

Sinhababu²,

Nayyar³

et al. 2022

Computers, Materials &Amp; Continua

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Sentiment analysis attracts the attention of Egyptian Decisionmakers in the education sector. It offers a viable method to assess education quality services based on the students' feedback as well as that provides an understanding of their needs. As machine learning techniques offer automated strategies to process big data derived from social media and other digital channels, this research uses a dataset for tweets' sentiments to assess a few machine learning techniques. After dataset preprocessing to remove symbols, necessary stemming and lemmatization is performed for features extraction. This is followed by several machine learning techniques and a proposed Long Short-Term Memory (LSTM) classifier optimized by the Salp Swarm Algorithm (SSA) and measured the corresponding performance. Then, the validity and accuracy of commonly used classifiers, such as Support Vector Machine, Logistic Regression Classifier, and Naive Bayes classifier, were reviewed. Moreover, LSTM based on the SSA classification model was compared with Support Vector Machine (SVM), Logistic Regression (LR), and Naive Bayes (NB). Finally, as LSTM based SSA achieved the highest accuracy, it was applied to predict the sentiments of students' feedback and evaluate their association with the course outcome evaluations for education quality purposes.

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Section: Naive Bayesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Course Evaluation Based on Deep Learning and SSA Hyperparameters Optimization

Pramanik¹,

Sinhababu²,

Nayyar³

et al. 2022

Computers, Materials &Amp; Continua

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“…Sentiment analysis itself has become a popular topic to be made because sentiment analysis can be applied in many industrial sectors, include the tourism industry [6]. Tourism sector in Indonesia itself always experience improvements every year and this tourism sector now ranked 42nd in the world [7]. One purpose famous tourism both in Indonesia and the world is the city of Bali, no Undoubtedly Bali City has a rich and diverse cultural heritage and the beauty of natural panoramas [8].…”

Section: Introductionmentioning

confidence: 99%

“…One purpose famous tourism both in Indonesia and the world is the city of Bali, no Undoubtedly Bali City has a rich and diverse cultural heritage and the beauty of natural panoramas [8]. technology has become one important factor in improving the tourism industry sector in Indonesia, this is because in this digital age almost all people are connected to it social media as long as they are traveling [7]. In some last year, twitter became one of many social media that used by a lot of people, Twitter is one of the media social and a service microblogging which allows its users to send a message real time [2].…”

Section: Introductionmentioning

confidence: 99%

“…In some last year, twitter became one of many social media that used by a lot of people, Twitter is one of the media social and a service microblogging which allows its users to send a message real time [2]. With twitter, public sentiment or opinion on tourism in a city can be known because inside tweet someone often conceives important information from an event that is very valuable to use as a tool to find out public opinion about these attractions, in addition that by using twitter can be seen the event or topic of the discussion currently popular with regard to tourism in the city by using hashtags [7].…”

Section: Introductionmentioning

confidence: 99%

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The Right Sentiment Analysis Method of Indonesian Tourism in Social Media Twitter

Steven

Wella

2020

International Journal of New Media Technology

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The growth of social media is changing the way humans communicate with each other, many people use social media such as Twitter to express opinions, experiences and other things that concern them, where things like this are often referred to as sentiments. The concept of social media is now the focus of business people to find out people's sentiments about a product or place that will become a business. Sentiment Analysis or often also called opinion mining is a computational study of people's opinions, appraisal, and emotions through entities, events and attributes owned. Sentiment analysis itself has recently become a popular topic for research because sentiment analysis can be applied in many industrial sectors, one of which is the tourism industry in Indonesia. To be able to do a sentiment analysis requires mastery of several techniques such as techniques for doing text mining, machine learning and natural language processing (NLP) to be able to process large and unstructured data coming from social media. Some methods that are often used include Naive Bayes, Neural Networks, K-Nearest Neighbor, Support Vector Machines, and Decision Tree. Because of this, this research will compare these four algorithms so that an algorithm can be used to analyze people's sentiments towards the city of Bali.

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