Naïve Bayes Classifiers For Tweet Sentiment Analysis Using GPU 
Islamiyah1, Nataniel Dengen2, Eny Maria3
1Islamiyah, Faculty of Computer Science and Information Technology, Mulawarman University, East Kalimantan, Indonesia.
2Nataniel Dengen, Faculty of Computer Science and Information Technology, Mulawarman University, East Kalimantan, Indonesia.
3Eny Maria, State Polytechnic Agricultural of Samarinda, East Kalimantan, Indonesia.
Manuscript received on 03 September 2019 | Revised Manuscript received on 13 September 2019 | Manuscript Published on 23 September 2019 | PP: 1470-1472 | Volume-8 Issue-5C, May 2019 | Retrieval Number: E12160585C19/19©BEIESP | DOI: 10.35940/ijeat.E1216.0585C19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The use of computers to solve problems has been done for all areas of work. Along with this, demanded faster computing process. To perform sentiment analysis of data obtained from the internet. Data taken from micro-blogging which at this time became the most popular communication tool and favored by internet users. The method used to construct the classification model of training data in this research is Naive Bayes Method. Training data is collected by utilizing the crontab facility with query emoticons and national media accounts linked to the Twitter API. The collected data will pass certain preprocessing before the training. The weighting feature used is the term frequency with TF-IDF. All data used in this research is a tweet that is delivered in Bahasa Indonesia. From the implementation results obtained 96.61% accuracy for sequential classification conducted using GPU GeForce 930M.
Keywords: GPU, Sentiment Analysis, Microblogging.
Scope of the Article: Parallel Computing on GPU