Abstract
Sentiment analysis or can also be called opinion mining is one of the main tasks of Natural Language Processing (NLP) which is a computational study that studies a person's opinion on a topic or entity. The analysis was performed with machine learning algorithms Naïve Bayes, Decision Tree, and K-Nearest Neighbor by dividing sentiment into two categories of sentiment namely positive sentiment and negative sentiment. The analysis data was taken from Financial Opinion Mining and Question Answering (FiQA) and The Financial PhraseBank which consisted of 4,840 sentences selected from various financial news and annotated by 16 different annotators experienced in the financial domain. This research is aimed at obtaining sentiment analysis results with the best algorithms through comparison of the performance of Naïve Bayes, Decision Tree, and K-Nearest Neighbor machine learning algorithms against financial sentences presented by FiQA and The Financial PhraseBank. Based on the analysis, the performance results of each algorithm were obtained with the accuracy value of the Naïve Bayes algorithm of 78,45%; Decision Tree algorithm with an accuracy value of 77,72%; K-Nearest Neighbor algorithm (k=3) with an accuracy value of 41,25%; and K-Nearest Neighbor (k=5) with an accuracy value of 37,38%. Sentiment analysis with the Naive Bayes algorithm (K=5) performs best with the highest accuracy values.
Recommended Citation
Brilianto, M. N., Susanti, Y., & Zukhronah, E. (2024). Analisis Sentimen terhadap Kalimat Finansial pada FiQA dan The Financial PhraseBank. PYTHAGORAS : Jurnal Matematika dan Pendidikan Matematika, 18(1). Retrieved from https://scholarhub.uny.ac.id/pythagoras/vol18/iss1/10
