DOI: https://doi.org/10.21456/vol14iss4pp353-362

Impact Of Sarcasm Detection on Sentiment Analysis Using Bi-LSTM and FastText

Junita Amalia orcid scopus  -  Information System, Institut Teknologi Del, Sitoluama, Indonesia
*Dian Filia Matondang orcid  -  Information System, Institut Teknologi Del, Sitoluama, Indonesia
Gibert E.M. Hutajulu  -  Information System, Institut Teknologi Del, Sitoluama, Indonesia
Agustina Hasibuan  -  Information System, Institut Teknologi Del, Sitoluama, Indonesia
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Abstract

Sentiment analysis categorizes a collection of texts in a document as either positive or negative. However, sometimes it cannot give accurate results due to sarcastic sentences. Sarcasm involves the use of positive language to convey negative meanings, So sarcasm detection is needed for sentiment classification to provide better results. One method that can be used to perform Sentiment classification is Bidirectional Long Short-Term Memory (Bi-LSTM). However, text data cannot be processed by Bi-LSTM, so it requires word embedding to convert text data into vectors. In this study, the word embedding used is FastText because it can learn the form of words by considering subword information. The results showed that sentiment classification with sarcasm detection could improve evaluation results by 0.08 for precision, 0.07 for recall, 0.07 for F1-score, and 0.07 for accuracy. A paired sample t-test was conducted on precision, recall, F1-score, and accuracy to examine whether there is a difference between sentiment classification with and without sarcasm detection. The obtained p-values are 2.84.10-9, 4.63.10-7, and 2.40.10-8, 6.22.10-8, respectively. This indicates a difference between sentiment classification with and without sarcasm detection. Therefore, with a 95% confidence level, it can be concluded that sarcasm detection impacts sentiment classification.

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Keywords: Bidirectional Long Short-Term Memory; FastText; Accuracy; Sentiment; Sarcasm; Classification.
Funding: Institut Teknologi Del

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Section: Research Articles
Language : EN
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