Sentiment Analysis of Elon Musk's Twitter Data Using LSTM and ANFIS-SVM


Erkartal B., Yilmaz A.

4th International Conference on Intelligent and Fuzzy Systems (INFUS), Bornova, Turkey, 19 - 21 July 2022, vol.505, pp.626-635 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 505
  • Doi Number: 10.1007/978-3-031-09176-6_70
  • City: Bornova
  • Country: Turkey
  • Page Numbers: pp.626-635
  • Keywords: ANFIS, LSTM, Elon musk
  • Istanbul Technical University Affiliated: No

Abstract

Social media plays a huge role spreading words to millions and influencing their opinions. Twitter is one of the most essential platform that reach over 300 million active users and 500 million tweets per day, it plays a significant role spreading the word around the world,. These tweets covers a various subjects from personal conversations to globally important topics such as updates about Covidl9 and macroeconomic subjects. Especially in financial matters, it is a very common situation that business owners, even politicians report the news on Twitter first. The Tesla's and SpaceX's CEO and owner Elon Musk's tweets had a huge impact on coin market or even stock exchanges. Although many accused him of market manipulation his tweets impact cannot be underestimated. In 2020 and 2021 there are various tweets that strike the stock market instantly both in the positive and negative direction. This study aims to predict the direction of his tweets and perform a sentiment analysis using both Long-Short Term Memory (LSTM) and Adaptive Neuro Fuzzy Interface Systems (ANFIS)-SVM(Support Vector Machines) models. The dataset is obtained by using Twitter API which spans a time horizon of 5 years. In order to compare the results under same conditions same preprocessing steps are performed for both models. According to the results, LSTM performs a superior performance with its 72.2% accuracy against ANFIS-SVM model with 74.1%.