SENTIMENT ANALYSIS ON TWITTER SOCIAL MEDIA ACCOUNTS: SHOPEECARE USING NAIVE BAYES, ADABOOST, AND SVM(EVOLUTION) ALGORITHM COMPARATIVE METHODS
The growth of Indonesian e-commerce is increasing along with the growth of internet use in Indonesia. In 2015, there were 92 million internet users in Indonesia. One of the popular online shopping platforms in Indonesia is Shopee. One of the services to see the response and reporting of problems from users, including shopeecare. shopeecare was created on the social media platform twitter to help facilitate communication between customers. with the amount of customer enthusiasm in tweeting and Retweeting existing content, we decided to research about Sentiment analysis on twitter social media accounts: Shopeecare uses the SMOTE NB, ADboost, and SVM comparison methods. From the data, the comparison results from the test experiments used the Smote + Naive Bayes, Smote + Naive Bayes + Adaboost, and Smote + SVM models. It is known that the Accuracy, Precision, AUC values of the Smote + SVM algorithm are higher than other algorithms, namely Accuracy 76.24%, Precision 75.65%, AUC 0.822. From the results of the algorithm comparison, it shows that the algorithm in determining the sentiment of the complaint and not complaint analysis is better than other algorithms.
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