ANALISIS SENTIMEN KOMENTAR INSTAGRAM MENGGUNAKAN LONG SHORT-TERM MEMORY (LSTM) DAN WORD2VEC

AHMAD, SANDY ARIB and Abdiansah, Abdiansah and Yusliani, Novi (2023) ANALISIS SENTIMEN KOMENTAR INSTAGRAM MENGGUNAKAN LONG SHORT-TERM MEMORY (LSTM) DAN WORD2VEC. Undergraduate thesis, Sriwijaya University.

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Abstract

Social media is a means for people to express their opinions on various topics that occur in the world. One of the most widely used social media is Instagram. Opinions in the form of comments on Instagram are generally written using abbreviated and non-standard language. In analyzing these comments, a method is needed to sort the comments to make it easier to determine the sentiment of the comments. Long Short-Term Memory (LSTM) along with Word Embedding Word2Vec is one of the deep learning methods that are widely used in sentiment analysis research. The result of this research is a model that produces 91% accuracy, 92.70% precision, 89% recall, 90.81% f-measure. Based on the test results, the LSTM method along with Word2Vec can be used to perform sentiment analysis of Instagram comments.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis Sentimen, Long Short-Term Memory, Word2Vec, Deep Learning
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Sandy Arib Ahmad
Date Deposited:	14 Aug 2023 06:32
Last Modified:	14 Aug 2023 06:32
URI:	http://repository.unsri.ac.id/id/eprint/127138

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