Development of a chatbot for the online application telegram chat with an approach to the emotion classification text using the IndoBERT-lite method
The increasing preference for text-based communication on online chat applications has caused the number of social interactions to increase rapidly. However, textbased communication usually results in misunderstandings resulting from the absence of feeling intonation and emotions in the text. T...
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| Main Authors: | , , |
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| Format: | Proceeding Paper |
| Language: | en en |
| Published: |
IEEE
2022
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/106191/7/106191_Development%20of%20a%20chatbot%20for%20the%20online%20application%20telegram.pdf http://irep.iium.edu.my/106191/8/106191_Development%20of%20a%20chatbot%20for%20the%20online%20application%20telegram_Scopus.pdf http://irep.iium.edu.my/106191/ https://ieeexplore.ieee.org/document/10031483 |
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| Summary: | The increasing preference for text-based
communication on online chat applications has caused the
number of social interactions to increase rapidly. However, textbased
communication usually results in misunderstandings
resulting from the absence of feeling intonation and emotions in
the text. This study aims to create a chatbot that can detect
emotions text to be entered into online chat applications. This
study used a pre-trained model specifically trained from a
collection of Indonesian-language datasets, namely IndoBERTlite.
The dataset used to train the model is a collection of
Indonesian tweets totaling 4,403 which have been labeled with 5 classes of emotions, namely love, happy, anger, sadness, and
fear. The hyperparameters used in this study to train the model
were 5 epochs, batch size 16, learning rate 0.000003, and adam
optimizer. Based on the test results with the parameters already
mentioned, the accuracy, F1 score, recall, and precision values
were obtained in the training set of 89%, 89%, 89%, and 90%,
while the validation set obtained 70%, 71%, 70%, and 72%. |
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