Implementation of Text-Mining For Sentiment Analysis Twitter With Support Vector Machine Algorithm
Text Mining can make it possible to analyze sentiments quickly. In this implementation, the process of capturing text is done with the help of the API. Twitter is used as an object of research because Twitter provides a medium for the process of retrieving data quickly by using keywords in the form of a word or hashtag. The form of implementation made in this study is the website. This website is made with the Django framework with the Python programming language. To produce sentiments, a data mining process is needed. This data mining process uses a support vector machine algorithm and this process takes place in the backend of a website. The level of accuracy generated by this data mining process is 73%. The purpose of this sentiment analysis is used so that the data that has been collected can be used as useful information.
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