ANALYSIS OF STORE CUSTOMER SENTIMENT USING THE NAIVE BAYES CLASSIFIER METHOD DHENDSUL STORE: (Case Study: Dhedsul Store) - Karolus Wulla Rato; Antonius L B Hallan; Gergorius Kopong Pati (CV. Fahr Publishing, 2023)
Sentiment Analysis is a technique for extracting text data to obtain information about positive, neutral or negative sentiment. Sentiment analysis is provided by internet users on social media to provide a personal assessment or opinion. The Dhedsul shop that often gets user sentiment through social media is the Dhedsul shop. The sentiment of opinions from consumers about the Dhedsul Shop can be analyzed and used to obtain useful information for other customers and the Dhedsul Shop. By using the Text Mining technique, the classification method will determine whether a sentiment is positive, neutral or negative. One algorithm that is widely used in sentiment analysis is the Naïve Bayes classification method. This research uses the Naïve Bayes Classifier (NBC) method with tf-idf weighting accompanied by the addition of an emotional icon (emoticon) conversion feature to determine the existing sentiment classes from tweets about the Dhedsul Store. The research results show that the Naïve Bayes method without adding features is able to classify sentiment with an accuracy value of 80.85%, while if the tf-idf weighting feature is added along with emotional icon conversion it is able to increase the accuracy value to 81%.