Sentiment Analysis On Tripadvisor Travel Agent Using Random Forest, Support Vector Machines, and Naïve Bayes Methods
TripAdvisor faces problems in improving the quality of service on its application, namely the presence of unexpected or non-functional features, which can affect the user experience and reduce trust in the application. This research aims to develop an application capable of performing sentiment an...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Center for Research and Community Service, Institut Informatika Indonesia Surabaya
2025-03-01
|
| Series: | Teknika |
| Subjects: | |
| Online Access: | https://ejournal.ikado.ac.id/index.php/teknika/article/view/1198 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850240687075753984 |
|---|---|
| author | Ariq Ammar Fauzi Anik Vega Vitianingsih Slamet Kacung Anastasia Lidya Maukar Seftin Fiti Ana Wati |
| author_facet | Ariq Ammar Fauzi Anik Vega Vitianingsih Slamet Kacung Anastasia Lidya Maukar Seftin Fiti Ana Wati |
| author_sort | Ariq Ammar Fauzi |
| collection | DOAJ |
| description |
TripAdvisor faces problems in improving the quality of service on its application, namely the presence of unexpected or non-functional features, which can affect the user experience and reduce trust in the application. This research aims to develop an application capable of performing sentiment analysis on TripAdvisor application user reviews on the Google Play Store with negative, positive, and neutral classifications using the Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB). The RF method was chosen in this study because of its ability to handle large and complex data very accurately, while SVM is able to classify data on a large scale and is resistant to overfitting, while NB is able to classify text with clear probabilities. The Lexicon-based method as data labelling. The results of sentiment analysis from 1500 reviews with web scrapping show the classification of positive, negative, and neutral sentiments of 48, 726, and 646 data, respectively. Model performance in RF, SVM, and NB testing gets an accuracy value of 94%, 93.6%, and 77.8%, respectively. The RF model produces the best accuracy compared to other methods. The RF model produces the best accuracy compared to other methods. The results of sentiment analysis from 1500 user reviews allow developers to identify features that are often criticized or do not function properly in their application services.
|
| format | Article |
| id | doaj-art-bc25845d492044afa7e31cb7312b245e |
| institution | OA Journals |
| issn | 2549-8037 2549-8045 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Center for Research and Community Service, Institut Informatika Indonesia Surabaya |
| record_format | Article |
| series | Teknika |
| spelling | doaj-art-bc25845d492044afa7e31cb7312b245e2025-08-20T02:00:47ZengCenter for Research and Community Service, Institut Informatika Indonesia SurabayaTeknika2549-80372549-80452025-03-0114110.34148/teknika.v14i1.1198Sentiment Analysis On Tripadvisor Travel Agent Using Random Forest, Support Vector Machines, and Naïve Bayes MethodsAriq Ammar Fauzi0Anik Vega Vitianingsih1Slamet Kacung2Anastasia Lidya Maukar3Seftin Fiti Ana Wati4Informatics Department, Universitas Dr. Soetomo, Surabaya, East Java, IndonesiaInformatics Department, Universitas Dr. Soetomo, Surabaya, East Java, IndonesiaInformatics Department, Universitas Dr. Soetomo, Surabaya, East Java, IndonesiaIndustrial Engineering Department, President University, Bekasi, West Java, IndonesiaInformation System Department, Universitas Pembangunan Nasional Veteran Jawa Timur, Surabaya, East Java, Indonesia TripAdvisor faces problems in improving the quality of service on its application, namely the presence of unexpected or non-functional features, which can affect the user experience and reduce trust in the application. This research aims to develop an application capable of performing sentiment analysis on TripAdvisor application user reviews on the Google Play Store with negative, positive, and neutral classifications using the Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB). The RF method was chosen in this study because of its ability to handle large and complex data very accurately, while SVM is able to classify data on a large scale and is resistant to overfitting, while NB is able to classify text with clear probabilities. The Lexicon-based method as data labelling. The results of sentiment analysis from 1500 reviews with web scrapping show the classification of positive, negative, and neutral sentiments of 48, 726, and 646 data, respectively. Model performance in RF, SVM, and NB testing gets an accuracy value of 94%, 93.6%, and 77.8%, respectively. The RF model produces the best accuracy compared to other methods. The RF model produces the best accuracy compared to other methods. The results of sentiment analysis from 1500 user reviews allow developers to identify features that are often criticized or do not function properly in their application services. https://ejournal.ikado.ac.id/index.php/teknika/article/view/1198TripAdvisor Sentiment AnalysisRandom ForestSupport Vector MachineNaïve Bayes |
| spellingShingle | Ariq Ammar Fauzi Anik Vega Vitianingsih Slamet Kacung Anastasia Lidya Maukar Seftin Fiti Ana Wati Sentiment Analysis On Tripadvisor Travel Agent Using Random Forest, Support Vector Machines, and Naïve Bayes Methods Teknika TripAdvisor Sentiment Analysis Random Forest Support Vector Machine Naïve Bayes |
| title | Sentiment Analysis On Tripadvisor Travel Agent Using Random Forest, Support Vector Machines, and Naïve Bayes Methods |
| title_full | Sentiment Analysis On Tripadvisor Travel Agent Using Random Forest, Support Vector Machines, and Naïve Bayes Methods |
| title_fullStr | Sentiment Analysis On Tripadvisor Travel Agent Using Random Forest, Support Vector Machines, and Naïve Bayes Methods |
| title_full_unstemmed | Sentiment Analysis On Tripadvisor Travel Agent Using Random Forest, Support Vector Machines, and Naïve Bayes Methods |
| title_short | Sentiment Analysis On Tripadvisor Travel Agent Using Random Forest, Support Vector Machines, and Naïve Bayes Methods |
| title_sort | sentiment analysis on tripadvisor travel agent using random forest support vector machines and naive bayes methods |
| topic | TripAdvisor Sentiment Analysis Random Forest Support Vector Machine Naïve Bayes |
| url | https://ejournal.ikado.ac.id/index.php/teknika/article/view/1198 |
| work_keys_str_mv | AT ariqammarfauzi sentimentanalysisontripadvisortravelagentusingrandomforestsupportvectormachinesandnaivebayesmethods AT anikvegavitianingsih sentimentanalysisontripadvisortravelagentusingrandomforestsupportvectormachinesandnaivebayesmethods AT slametkacung sentimentanalysisontripadvisortravelagentusingrandomforestsupportvectormachinesandnaivebayesmethods AT anastasialidyamaukar sentimentanalysisontripadvisortravelagentusingrandomforestsupportvectormachinesandnaivebayesmethods AT seftinfitianawati sentimentanalysisontripadvisortravelagentusingrandomforestsupportvectormachinesandnaivebayesmethods |