Aplikasi Sentiment Analysis terhadap Trend Cryptocurrency pada Platform Twitter Menggunakan Library Textblob sebagai Alat Bantu Berinvestasi

Abstract

Along with the rapid development of information technology, many digital communication tools make it easier for people to access or share information. Twitter is one of the social media that has more than 1.3 billion users and more than 500 million tweets or tweets every day. The uniqueness of Twitter limits the number of writings to 280 characters, making Twitter a social media that contains sentiments about something. The cryptocurrency alone gets 4.1 million hashtag exposures per hour and has 2225 unique tweets per hour on the Twitter platform. The number of tweets related to Cryptocurrency causes investors to lose in terms of time because they have to manually assess a tweet. To overcome this, an effort that can be realized is to classify sentiments. One of the Natural Language Processing methods that have been developed for sentiment classification is TextBlob. In this thesis, an application will be made with sentiment analysis features using the Textblob Library, request tweets data using the Tweepy API, visualization of tweets data in the form of pie charts, tables, and word clouds, features that display the market price and history of cryptocurrency coins using the CoinGecko API and YFinance. as well as tweets from selected accounts. TextBlob Library testing is done by classifying results with 100 data that have been labeled by 2 examiners who have more than 1 year of experience investing in cryptocurrencies, the results obtained are 35% of the data have similarities between the results with the second tester, the application is tested with Tweets data request according to keywords, as well as application testing to display visualizations of Tweets data. A correlation test was conducted between the price change of cryptocurrency coins in 24 hours with the results of the classification of Tweet data and the Tweet volume of several coins. The conclusion that can be drawn from the correlation test is that when Tweet volume increases from the previous day, there will be a trend where the coin will increase or decrease. The results of the application web page where the application can display tweet data according to keywords and display visualizations, as well as display the price and history of the cryptocurrency market according to the available input.