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All Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Merinda Lestandy
Muhammadiyah Malang University
Computer Science & IT Education Engineering

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Effect of Word2Vec Weighting with CNN-BiLSTM Model on Emotion Classification Merinda Lestandy; Abdurrahim
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 1 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i1.58571

Abstract

Emotion is an element that can influence human behavior, which in turn influences a decision. Human emotion detection is useful in many areas, including the social environment and product quality. To evaluate and categorize emotions derived from text, a method is required. As a result, the CNN-BiLSTM model, a classification method, aids in the analysis of the text's emotional content. A word weighting technique employing word2vec as a word weighting will help the model. The CNN-BiLSTM model with Word2vec as a pre-trained model is being used in this study to find the findings with the highest accuracy. The information is split into two groups: training and testing, and it is categorized into six categories according to how each emotion manifests itself: surprise, sadness, rage, fear, love, and joy. The best outcome from the CNN-BiLSTM model's accuracy of emotion classification is 92.85%.
Co-Authors Abdurrahim