ANALISIS SENTIMEN PERILAKU MANUSIA PADA PRODUK DAN ORGANISASI MENGGUNAKAN K-MEANS DAN SVM - Samsuriah; Mawar; Syamsia (Nusantara Hasana Journal, 2024)
Sentiment analysis is the process of extracting information from text and is considered opinion mining. Machine learning experiments have been demonstrated in research involving reviews, blogs, for several texts written in different languages ​​including Dutch, English, and French. A set of example sentences was prepared which were manually labeled neutral, positive, or negative. This study covers consumer curiosity about certain consumer products. A categorization model has been developed that has been used in research. A number of issues involving the noisy nature of text have been addressed in research. With an accuracy of around 83%, positive, negative and neutral sentiment towards the subject under study can be determined using unigram features enriched with linguistic information. The role of active learning approaches in minimizing the number of examples that need to be manually annotated is discussed in this article. This research provides data on the transferability of the studied model. Sentiment analysis of a particular employee has been studied using K-Means Clustering and SVM classification to classify the sentiment of the text.