Exploring Indonesian Netizen's Emotional Behavior Through Investment Sentiment Analysis Using TextBlob-NLTK (Natural Language Toolkit)

Abstract

The investment industry has recently continued to provide the greatest experience and has grown the number of investors to this day. It also increases the quantity of traded investment assets because of its varied central and decentralized operating processes. Yet another aspect that can't be isolated from the investment process is price volatility and monetary policy. This means that the present price movement is influenced by every market mood. According to this study, a three-month period of tweets from Indonesian citizens was used to analyze attitude towards investment patterns in Indonesia. Because of the large number of people throughout the world who use Twitter to voice their opinions on investments, Twitter was selected as the primary source for this study. Twint, an open-source Python library, is used to retrieve tweet data. To process and analyze each tweet's data, TextBlob will be used, which values subjectivity and polarity. There were 92% favorable feelings and 42% positive sentiments on Indonesian tweets after a succession of research stages. These results were obtained by the limitation of data preprocessing and data labeling has been used.