[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127307-en":3,"doc-seo-127307-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127307,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786593998035447633",8,"Research & Report","GoPay App Review Sentiment Classification Optimization Using a Combination of Text Representation and Machine Learning","GoPay app reviews in Indonesia reflect user perceptions amid intense competition, yet understanding those perceptions remains challenging for developers. This study builds a sentiment analysis model by comparing text representation methods (TF-IDF vs. BERT) and machine learning algorithms (Random Forest vs. Logistic Regression). Google Play Store review data are preprocessed, transformed into features, and modeled for sentiment prediction. Results show BERT plus Logistic Regression achieves the best F1 score (0.86) and highlights financial-related terms as key issues, supporting UX improvement.","| IJITEB\u003Cbr>Vol 6\u003Cbr>No. 2\u003Cbr>2024 | \u003Cbr>International Journal of Information Technology and Business\u003Cbr>[http://ejournal.uksw.edu/ijiteb](http://ejournal.uksw.edu/ijiteb)\u003Cbr> |  |\n| --- | --- | --- |\n| GoPay App Review Sentiment Classification Optimization Using a Combination of Text Representation and Machine Learning\u003Cbr>Rifki Dwi Kurniawan\u003Cbr>Master of Information Systems, Faculty of Information Technology, Satya Wacana Christian\u003Cbr>University,Indonesia |  |  |\n| Keywords: BERT, Logistic Regression, Random Forest, Sentiment Analysis, TF-IDF |  | Abstract: GoPay as one of the digital payment applications in Indonesia faces challenges in understanding user perceptions in the midst of fierce competition. This study aims to develop a user review sentiment analysis model by comparing two approaches to text representation, namely TF-IDF and BERT, as well as two machine learning algorithms, namely Random Forest and Logistic Regression. Review data is obtained from the Google Play Store and processed through pre-processing, feature extraction, and sentiment modeling. The results showed that the combination of BERT + Logistic Regression provided the best performance with an F1 Score of 0. 86, showing the superiority of BERT in understanding the semantic context compared to TF-IDF. An important feature analysis identifies financial-related words such as \"duitnyaapakah\"and \"kompensasi\" as key issues. This research makes a practical contribution by helping app developers improve the user experience through prioritizing relevant features and solutions to key problems complained of. |\n\n1. Introduction  \nIn the digital era, technology has changed various aspects of life, including the way people conduct financial transactions. GoPay, one of the main digital payment apps in Indonesia, has become an integral part of the financial ecosystem, allowing people to make payments more easily, quickly, and securely. Factors such as increased smartphone penetration, internet network expansion, and changing consumer preferences for cashless payment methods have driven the widespread adoption of GoPay in Indonesia [1] . However, in the midst of increasingly fierce competition with various other digital payment applications, GoPay faces a major challenge in maintaining the loyalty and satisfaction of its users. To compete effectively, GoPay developers need to understand user needs and preferences more deeply and respond to feedback quickly and accurately.  \nSentiment analysis of user reviews has become an effective method to understand the user experience of a service or application. Through user reviews, developers can identify complaints, suggestions, and features that are liked or disliked. This approach  \ninvolves using Natural Language Processing (NLP) technology to process unstructured text data such as user reviews, which typically contain important information related to customer perception [2]–[4] . In sentiment analysis, text representation plays an important role. One popular method is TF-IDF (Term Frequency-Inverse Document Frequency), which is used to measure the importance of a word in a given document relative to the entire corpus. TF-IDF is suitable for identifying keywords that are significant in user reviews [5], [6] . However, this method has limitations in understanding the semantic context between words.  \nAlternatively,Word Embeddings-based approaches such as BERT (Bidirectional Encoder Representations from Transformers) have emerged as more sophisticated solutions. BERT is able to understand semantic relationships between words in a sentence context bidirectionally, allowing the model to capture more complex meanings compared to frequency-based methods such as TF-IDF [7] . BERTbased representations have proven effective in a variety of text analysis applications, including  \nsentiment classification and information mining [8],[9] .  \nIn addition to text representation techniques, the selection of machine learning algorithms is al","cbCaiaUAD6fTSRmP","https://ap.wps.com/l/cbCaiaUAD6fTSRmP","pdf",878364,1,6,"English","en",105,"# Introduction\n## Sentiment analysis and user reviews\n## Text representation: TF-IDF vs BERT\n## Machine learning algorithms: Random Forest vs Logistic Regression\n## Research gap and study objective","[{\"question\":\"What problem does the research address regarding GoPay?\",\"answer\":\"It addresses the difficulty of understanding user perceptions from app reviews while competition among digital payment applications remains intense.\"},{\"question\":\"Which text representation and machine learning combinations are compared?\",\"answer\":\"TF-IDF is compared with BERT, and Random Forest is compared with Logistic Regression for sentiment modeling.\"},{\"question\":\"What is the best-performing approach and how is performance measured?\",\"answer\":\"The combination of BERT + Logistic Regression provides the best performance, achieving an F1 score of 0.86.\"}]","GoPay App Review Sentiment Classification Optimization Using a Combination of Text Representation and Machine Learning | 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