Contextual Emotion Detection in Text using Deep Learning and Big Data

Chandra, Papel; Ahammed, Md. Tabil; Ghosh, Sudipto; Emon, Rabiul Hasan; Billah, Mostasim; Ahamad, Md. Ifran; Balaji, Priyadharshini

doi:10.1109/iccsea54677.2022.9936154

Cited by 6 publications

(2 citation statements)

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“…The deep learning classifiers used primarily in TBED are briefly described below. The LSTM and its variants have been demonstrated to be very beneficial [208]. Gated Recurrent Unit (GRU): An easier design than the LSTM is the GRU architecture.…”

Section: (C) Deep Learningmentioning

confidence: 99%

Challenges and Opportunities of Text-Based Emotion Detection: A Survey

Maruf,

Khanam,

Haque

et al. 2024

IEEE Access

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Emotion detection has become an intriguing issue for researchers because of its psychological, social, and commercial significance. People express their feelings directly or indirectly through facial expressions, language, writing, or behavior. An emotion detection tool is a critical and practical way of recognizing and categorizing moods with various applications. Artificial intelligence is often used in research to identify emotions. Machine learning and deep learning algorithms produce high-quality solutions for diagnosing emotional diseases in social media users. Numerous studies and survey articles have been published on emotion detection based on textual data. However, most of these studies did not comprehensively address emerging architectures and performance analysis in emotion detection. This paper provides an extensive survey of state-of-the-art systems, techniques, and datasets for textual emotion recognition. Another goal of this study is to emphasize the limitations and provide up-and-coming research directions to fill these gaps in this rapidly evolving field. This survey paper investigated the concepts and the performances of different categories of textual emotion detection models, approaches, and methodologies.

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Section: (C) Deep Learningmentioning

confidence: 99%

Challenges and Opportunities of Text-Based Emotion Detection: A Survey

Maruf,

Khanam,

Haque

et al. 2024

IEEE Access

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“…The author of this paper [10] presents a novel approach to the task of detecting emotions in text using deep learning and big data. The paper first outlines the challenges of emotion detection in text and the need for better approaches to the task.…”

Section: Related Workmentioning

confidence: 99%

Emoji Prediction Using Bi-Directional LSTM

Kone,

Anagal,

Anegundi

et al. 2023

ITM Web Conf.

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Messengers and social media dominate today’s internet usage across the globe. For the large population, a typical day starts with messages flooding on mobiles, from simple good morning wishes, business meeting invites, reminders, and schedules for the day and the list is endless. A striking feature of today’s digital communication is the variety of emojis used, without which text communication almost look incomplete. Emojis are graphic symbols/logograms used with text communication to enhance the effectiveness of emotions and set an undertone that makes texting a more fun experience for the users. Emojis are the visual language of the new generation. They give consumers a means to communicate their feelings while reducing the quantity of text that needs to be typed by the sender. Every social media and messenger platform like Facebook, Instagram, Twitter, WhatsApp, and many more have its own emoji set. To lure more and more users, many new emojis are added day by day. Predicting and suggesting emojis based on the text, emotion and user patterns to the user is an important feature of today’s messengers and social media applications. If you start typing a message, relevant emojis will be displayed from which users can choose an emoji, further enhancing the user texting experience. This process is done using natural language processing and machine learning techniques. In this paper, we study emoji prediction techniques and propose an emoji prediction model using bi-directional LSTMs. We compare emoji prediction NLP techniques, including RNN, LSTM, LSTM networks, and Bi-LSTM. Based on our implementation, we suggest that the bi-directional LSTM model is the most effective technique. Our model outperforms many baseline approaches with an accuracy of 94% when tested on a CodaLab Twitter data set with 60000 rows and two columns. Our study shows the effectiveness and efficiency of bi-directional LSTMs for text-based systems for communication.

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