Perancangan Klasifikasi Tweet Berdasarkan Sentimen Dan Fitur Calon Gubernur DKI Jakarta 2017 - Sumarni Adi (Journal Of Informatic Pelita Nusantara, 2018)
One of the fastest growing social media users is Twitter, the number of twitter users mentioned continues to increase 300,000 users every day [1]. Twitter users send twitter posts about the facts and opinions of the government products or services they use or express their political, ideological and interest views. Not to mention also send tweet opinions related leaders or influential public figures in this country. With 55 million tweets each day Twitter has a high update rate [1] and is a highly efficient data warehouse for political and social research, so Twitter is a good place to conduct opinion mining or sentiment analysis in classifying the 2017 Jakarta governor candidate .The classification of tweet data is done by analyzing the sentiments on Indonesian tweet opinions by extracting features using Unigram, negation, term Frequency, and TF-IDF (Term Frequency-Invers Document Frequency). Once extracted, the tweet is classified using the Naïve Bayes Classifier (NBC) algorithm.From the results of designing the twitter classification of Indonesian language using Naïve Bayes Classifier algorithm obtained significant difference in value when compared with manual labeling. Positive and neutral sentiments are significant, while negative sentiments are not significant. Keywords: tweet, sentiment, classification, Naïve Bayes Classifier (NBC)