<![CDATA[Sentiment analysis is frequently used to examine textual data to determine the sentiment contained in a sentence. This method can be used to determine the polarity of emotions associated with specific themes, events, products, and certain phenomena in the world. This study utilized Twitter data to analyze textual data in Bahasa Indonesia gathered via Twitter using R. This study will focus on ten tourism destinations. These destinations were targeted for development by the Indonesian government in 2016 through the Ministry of Tourism. The purpose of this study was to determine the sentiment polarity of Indonesian Twitter users towards ten tourism destinations. The sentiment polarity score was calculated using an Indonesian sentiment lexicon. The results of this study indicate that the Indonesian government’s designation of a tourism destination as a development priority destination appears to have no effect on the tourism destination’s sentiment polarity, particularly among Twitter users. The popularity status of a tourist destination does not translate to a high score on positive sentiment. Due to the limitation of the sentiment lexicon in Bahasa Indonesia, it can be inferred from this study that getting an accurate sentiment score for each tweet is still not achievable. However, the sentiment lexicon in Bahasa Indonesia should be updated to increase the accuracy of the sentiment score calculation.Keywords: Indonesian sentiment analysis, Indonesian sentiment lexicon, Tourism destinations.]]>
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