Hierarchical Bi-LSTM based emotion analysis of textual data

• Autorzy: Mahto Dashrath , Yadav Subhash Chandra

Identyfikatory

DOI

10.24425/bpasts.2022.141001

• Języki publikacji: EN

Abstrakty

EN

Nowadays, Twitter is one of the most popular microblogging sites that is generating a massive amount of textual data. Such textual data is intended to incorporate human feelings and opinions with related events like tweets, posts, and status updates. It then becomes difficult to identify and classify the emotions from the tweets due to their restricted word length and data diversity. In contrast, emotion analysis identifies and classifies different emotions based on the text data generated from social media platforms. The underlying work anticipates an efficient category and prediction technique for analyzing different emotions from textual data collected from Twitter. The proposed research work deliberates an enhanced deep neural network (EDNN) based hierarchical Bi-LSTM model for emotion analysis from textual data; that classifies the six emotions mainly sadness, love, joy, surprise, fear, and anger. Furthermore, the emotion analysis result obtained by the proposed hierarchical Bi-LSTM model is being compared and validated with the traditional hybrid CNN-LSTM approach regarding the accuracy, recall, precision, and F1-Score. It can be observed from the results that the proposed hierarchical Bi-LSTM achieves an average accuracy of 89% for emotion analysis, whereas the existing CNN-LSTM model achieved an overall accuracy of 75%. This result shows that the proposed hierarchical Bi-LSTM approach achieves desired performance compared to the CNN-LSTM model.

Słowa kluczowe

EN

emotion analysis; machine learning; emotion detection; deep learning; hierarchical Bi-LSTM

PL

analiza emocji; uczenie maszynowe; wykrywanie emocji; głęboka nauka; hierarchiczna dwukierunkowa pamięć krótkotrwała

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2022
• Tom: Vol. 70, nr 3
• Strony: art. no. e141001
• Opis fizyczny: Bibliogr. 33 poz., rys., tab.

Twórcy

autor

Mahto Dashrath

• Department of Computer Science and Technology, Central University of Jharkhand, Ranchi, India

• https://orcid.org/0000-0002-7604-4291

autor

Yadav Subhash Chandra

• Department of Computer Science and Technology, Central University of Jharkhand, Ranchi, India

• https://orcid.org/0000-0002-1075-6384

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).