Microblogging sentiment analysis with lexical based and machine learning approaches

The Digital World encounters rapid development nowadays, especially through the proliferation of social media in Indonesia. Twitter has become one of social media with expanded users within every sectors of society. There are so many part both individual as well as organization/enterprise which utilize twitter as tool for communication, business, customer relation, and other activities. Through the twitter´s ever-expanding users with those particular purposes, the precise method to effectively and efficiently analyzing opinion-contained sentences become crucially needed. Therefore this research made for method analyzing through lexical based and model based approaches by machine learning to classify opinion-contained tweets using those 2 methods. The tested machine learning method are Support Vector Machine (SVM), Maximum Entropy (ME), Multinomial Naive Bayes (MNB), and k-Nearest Neighbor (k-NN). Based on the test outcome, lexical based approach highly depended on lexical database which became opinion classification matrix. Whilst machine learning approach can produce better accuracy due to its capability in new training data modeling based on outcome model. However, machine learning model based approach depends on various factors in analyzing sentiment.