Title

Emotion classification in poetry text using deep neural network

Document Type

Article

Source of Publication

Multimedia Tools and Applications

Publication Date

3-26-2022

Abstract

Emotion classification from online content has received considerable attention from researchers in recent times. Most of the work in this direction has been carried out on classifying emotions from informal text, such as chat, sms, tweets and other social media content. However, less attention is given to emotion classification from formal text, such as poetry. In this work, we propose an emotion classification system from poetry text using a deep neural network model. For this purpose, the BiLSTM model is implemented on a benchmark poetry dataset. This is capable of classifying poetry into different emotion types, such as love, anger, alone, suicide and surprise. The efficiency of the proposed model is compared with different baseline methods, including machine learning and deep learning models.

ISSN

Publisher

Springer Science and Business Media LLC

First Page

1

Last Page

22

Disciplines

Computer Sciences

Keywords

Emotion detection, Poetry, Deep learning, BiLSTM

Indexed in Scopus

yes

Open Access

no

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