Comparison of Accuracy between Long Short-Term Memory-Deep Learning and Multinomial Logistic Regression-Machine Learning in Sentiment Analysis on Twitter

The paper is about sentiment analysis research on Twitter. In this research data with the keyword, ‘Russian Hacking’ concerning the 2016 US presidential election on Twitter was taken as a dataset using Twitter API with Python pro-gramming language. The first process in sentiment analysis is the cleaning phase of tweet data, then using the Lexicon-based method to produce positive, negative, and neutral sentiment values for each tweet. Data that has been cleaned and classified will be processed in the Deep learning method with Long Short-Term Memory (LSTM) algorithm and Machine learning method with Multinomial Logistic Regression (MLR) algorithm. The accuracy of these two classification methods are calculated using the confusion-matrix method. The accuracy obtained from the LSTM classification method is 93 % and the MLR classification method is 92 %. Thus, it can be concluded that LSTM is better in classifying sentiments compared to MLR.

@article{Muslim2020,
  title     = {Comparison of Accuracy between Long Short-Term Memory-Deep Learning and Multinomial Logistic Regression-Machine Learning in Sentiment Analysis on Twitter},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {2},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Aries Muslim and Achmad Benny Mutiara and Rina Refianti and Cut Maisyarah Karyati and Galang Setiawan},
  doi       = {10.14569/IJACSA.2020.0110294},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110294}
}