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Evaluations of Emotion Analysis of Tweets using Bidirectional Long Short Term Memory and Conventional Machine Learning

Universitas Nusa Mandiri, Indonesia

Received: 29 Mar 2021; Published: 30 Apr 2022.
Open Access Copyright (c) 2022 Aloysius Kurniawan Santoso, Aliyah Kurniasih, Bagus Dwi Wicaksono, Hilman F Pardede
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

Many ideas are contained in the social media twitter as a form of expression for an event. This review can be used to determine a person's emotions based on text data so that we can determine the next action in addressing and responding to that opinion. Emotion classification on twitter can be done by recognizing the tweet text pattern of the user. In this study, representing emotions using the BiLSTM model and the Conventional Machine Learning model. The amount of learning rate and the number of layers and the optimizer used and the number of epochs in the BiLSTM model can affect the accuracy results. In the conventional machine learning model, the K value of the KNN, the selection of the naive bayes model on probalistic, and the Decision Tree variation in the values of Max-depth, min-leaves, min-split will affect the results of the accuracy value. So that we get a good model for the classification of emotional sentiments based on text data from an opinion on the tweets page.

 

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Keywords: Tweet; Emotion; BiLSTM; Machine Learning

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