[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119817-en":3,"doc-seo-119817-105":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},119817,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","SENTIMENT LABELING AND TEXT CLASSIFICATION MACHINE LEARNING FOR WHATSAPP GROUP","WhatsApp Group (WAG) communication generates increasing amounts of unstructured text that can be analyzed for sentiment in different perspectives. This research investigates the potential of the SentiWordNet lexicon to label positive, negative, and neutral sentiment for WAG data from “Alumni94”, then trains and tests multiple machine-learning text classification models. Six models are evaluated: Random Forest, Decision Tree, Logistic Regression, KNN, Linear SVM, and Artificial Neural Network. Neutral sentiment dominates (7588 samples), followed by negative (324) and positive (1617). Random Forest achieves the strongest precision and recall (83% and 64%), while Decision Tree shows slightly lower precision and recall (80% and 66%) with an improved f-measure (71%). Classification accuracy for Random Forest and Decision Tree reaches 89% for new messages, supporting the combined use of SentiWordNet and machine learning for WAG sentiment analysis.","SENTIMENT LABELING AND TEXT CLASSIFICATION MACHINE LEARNING FOR WHATSAPP GROUP  \nSusandri Susandri1*; Sarjon Defit2; Muhammad Tajuddin3  \nFaculty of Computer Science1  \nSTMIK Amik Riau  \n[https://sar.ac.id/](https://sar.ac.id/)  \n[susandri@sar.ac.id](susandri@sar.ac.id1)[1](susandri@sar.ac.id1)*  \nFaculty of Computer Science 2  \nUPIYPTK Padang  \n[https://upiyptk.ac.id/](https://upiyptk.ac.id/)  \n[sarjonde@yahoo.co.uk](sarjonde@yahoo.co.uk2)[2](sarjonde@yahoo.co.uk2)  \nFaculty of Computer Science 3  \nBumigora University  \n[https://universitasbumigora.ac.id/](https://universitasbumigora.ac.id/)  \n[tajuddin@universitasbumigora.ac.id](tajuddin@universitasbumigora.ac.id2)[2](tajuddin@universitasbumigora.ac.id2)  \n(*) Corresponding Author  \n(Responsible for the Quality of Paper Content)  \nAbstract—The use of WhatsApp Group (WAG) for communication is increasing nowadays. WAG communication data can be analyzed from various perspectives. However, this data is imported in the form of unstructured text files. The aim of this research is to explore the potential use of the SentiwordNet lexicon for labeling the positive, negative, or neutral sentiment of WAG data from \"Alumni94\" and training and testing it with machine learning text classification models. The training and testing were conducted on six models, namely Random Forest, Decision Tree, Logistic Regression, K-Nearest Neighbors (KNN), Linear Support Vector Machine (SVM), and Artificial Neural Network. The labeling results indicate that neutral sentiment is the majority with 7588 samples, followed by 324 negative and 1617 positive samples. Among all the models, Random Forest showed better precision and recall, i.e., 83% and 64%. On the other hand, Decision Tree had slightly lower precision and recall, i.e., 80% and 66%, but exhibited a better f-measure of 71%. The accuracy evaluation results of the Random Forest and Decision Tree models showed significant performance compared to others, achieving an accuracy of 89% in classifying new messages. This research demonstrates the potential use of the SentiwordNet lexicon and machine learning in sentiment analysis of WAG data using the Random Forest and Decision Tree models  \nKeywords: Label Sentiment; Whatsapp Group; Text Classification; Machine Learning.  \nIntisari—Penggunaan WhatsApp Group (WAG) untuk komunikasi semakin meningkat saat ini. Data komunikasi WAG dapat dianalisis dari berbagai sudut pandang. Namun data tersebut diimpor dalam bentuk file text tidak terstruktur. Penelitian bertujuan mencari potensi penggunaan Leksikon SentiwordNet untuk pelabeansentimen positif, negatif, atau netral data WAG “Alumni94” dan melatihserta menguji dengan model klasifikasi teks Machine learning. Pelatihan dan pengujian dilakukan pada enam model yaitu Random Forest, Decision Tree, Logistic Regression, K-Nearest Neighbors (KNN), Linear support vector machine (SVM), dan Artificial Neural Network. Hasil pelabelan data menunjukan sentimen Netural lebih mayoritas dengan komposisi 7588, 324 Negatif, dan 1617 Positif. Dari semua model Random Forest memberikan presisi, recall yang lebih baikyaitu 83%, 64% Sedangkan Decision Tree lebih rendah sedikit pada presisi, recall yaitu 80%, 66% dengan ukuran-fyang lebih baik yaitu 71%. Hasil evaluasi akurasi model random forest dan decision tree menunjukkan kinerja yang signifikan dibanding yang lain dengan akurasi 89% untuk mengklasifikasi pesan baru. Penelitian ini menunjukkan potensi penggunaan Leksikon SentiwordNet dan pembelajaran mesin  \ndalam analisis sentimen data WAG dengan model Random Forest dan Decision Tree Kata Kunci: Label Sentimen; Whatshapp Group; Klasisfikasi Teks; Machine Learning.  \nINTRODUCTION  \nText classification (TC), also known as text categorization, is the process of assigning textual data to well-organized groups. The TC automatically analyzes texts and assigns them to predetermined categories. TC are crucial for processing and extracting information from unstructured data [1],[2] . Cate","cbCaidinLX48IY8X","https://ap.wps.com/l/cbCaidinLX48IY8X","pdf",1346898,1,7,"English","en",105,"# Introduction\n## Text classification systems\n## Machine learning methods and social media text classification\n## Challenges of WhatsApp Group unstructured data\n# Research approach and evaluation\n## Sentiment labeling with SentiWordNet\n## Model training and testing\n## Performance comparison and accuracy results","[{\"question\":\"What data source and sentiment labels are used in the study?\",\"answer\":\"The study uses WhatsApp Group communication data from “Alumni94” and labels sentiment as positive, negative, or neutral.\"},{\"question\":\"Which machine learning models are trained and tested for text classification?\",\"answer\":\"Six models are evaluated: Random Forest, Decision Tree, Logistic Regression, K-Nearest Neighbors (KNN), Linear Support Vector Machine (SVM), and Artificial Neural Network.\"},{\"question\":\"Which model performed best and what accuracy was reported?\",\"answer\":\"Random Forest performed best overall with precision 83% and recall 64%, and both Random Forest and Decision Tree reached 89% accuracy for classifying new messages.\"}]","SENTIMENT LABELING AND TEXT CLASSIFICATION MACHINE LEARNING FOR WHATSAPP GROUP | 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data source and sentiment labels are used in the study?","Question",{"text":75,"@type":76},"The study uses WhatsApp Group communication data from “Alumni94” and labels sentiment as positive, negative, or neutral.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are trained and tested for text classification?",{"text":80,"@type":76},"Six models are evaluated: Random Forest, Decision Tree, Logistic Regression, K-Nearest Neighbors (KNN), Linear Support Vector Machine (SVM), and Artificial Neural Network.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and what accuracy was reported?",{"text":84,"@type":76},"Random Forest performed best overall with precision 83% and recall 64%, and both Random Forest and Decision Tree reached 89% accuracy for classifying new 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