Comparison of various ML and DL Models for Emotion Recognition using Twitter

Kumar, Hemant; Gowramma, Y P; Manjula, S H; Anil, D.; Smitha, N

doi:10.1109/icicv50876.2021.9388522

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…In the context of emotion analysis, CNNs and RNNs have been shown to perform well at accurately categorizing emotions expressed in tweets. Consequently, this approach has been used in several studies and has demonstrated promising results [36]. Several studies have examined emotion detection and recognition methods.…”

Section: Emotion Analysis Using Facial Datamentioning

confidence: 99%

Exploring Emotion Analysis Using Artificial Intelligence, Geospatial Information Systems, and Extended Reality for Urban Services

Rokhsaritalemi,

Sadeghi-Niaraki,

Choi

2023

IEEE Access

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The availability of human emotion data is a promising development for intelligent systems that can enhance the interaction between humans and technology. By combining emotions with new technologies-such as artificial intelligence (AI), geospatial information systems (GIS), and extended reality (XR)-a new generation of emotion-based intelligent systems could offer several benefits for various urban applications. Our research aims to contribute to this field by reviewing the relevant literature. Consequently, we proposed a 3-layer framework covering the basics of emotion analysis, technologies and models, privacy issues, and implementation environments. We categorized different emotion-technology combinations-such as AI, GIS, and XR-in urban services, and introduced the concept of emotionintelligent systems constructed based on the integration of emotion data and new technologies. We also discussed the challenges in developing emotionally aware intelligent systems, addressing the obstacles that must be overcome to achieve a more emotionally intelligent generation. These systems have the potential to revolutionize the way humans interact with technology and could open new avenues for future studies in this area.INDEX TERMS Emotion analysis, artificial intelligence (AI), geospatial information system (GIS), extended reality (XR).

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Section: Emotion Analysis Using Facial Datamentioning

confidence: 99%

Exploring Emotion Analysis Using Artificial Intelligence, Geospatial Information Systems, and Extended Reality for Urban Services

Rokhsaritalemi,

Sadeghi-Niaraki,

Choi

2023

IEEE Access

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“…The conventional machine learning models employed in this study for sentiment and emotion classification include Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and AdaBoost, as they are known for their good performance [26] and efficiency even for handling millions of tweets [27]. All the algorithms are trained in scikit-learn library in Jupyter Notebook in Anaconda, with default values for all parameters for all classifiers.…”

Section: Conventional Machine Learning Modelsmentioning

confidence: 99%

Sentiment Polarity and Emotion Detection from Tweets Using Distant Supervision and Deep Learning Models

Kastrati

Biba

Imran

et al. 2022

Lecture Notes in Computer Science

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Automatic text-based sentiment analysis and emotion detection on social media platforms has gained tremendous popularity recently due to its widespread application reach, despite the unavailability of a massive amount of labeled datasets. With social media platforms in the limelight in recent years, it's easier for people to express their opinions and reach a larger target audience via Twitter and Facebook. Large tweet postings provide researchers with much data to train deep learning models for analysis and predictions for various applications. However, deep learning-based supervised learning is data-hungry and relies heavily on abundant labeled data, which remains a challenge. To address this issue, we have created a large-scale labeled emotion dataset of 1.83 million tweets by harnessing emotion-indicative emojis available in tweets. We conducted a set of experiments on our distant-supervised labeled dataset using conventional machine learning and deep learning models for estimating sentiment polarity and multi-class emotion detection. Our experimental results revealed that deep neural networks such as BiLSTM and CNN-BiLSTM outperform other models in both sentiment polarity and multi-class emotion classification tasks achieving an F1 score of 62.21% and 39.46%, respectively, an average performance improvement of nearly 2-3 percentage points on the baseline results.

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Comparing Deep and Machine Learning Models for Sentiment and Emotion Classification from Vaccine #sideffects

Dubey

Gokhale

2022

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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Comparison of various ML and DL Models for Emotion Recognition using Twitter

Cited by 6 publications

References 11 publications

Exploring Emotion Analysis Using Artificial Intelligence, Geospatial Information Systems, and Extended Reality for Urban Services

Exploring Emotion Analysis Using Artificial Intelligence, Geospatial Information Systems, and Extended Reality for Urban Services

Sentiment Polarity and Emotion Detection from Tweets Using Distant Supervision and Deep Learning Models

Comparing Deep and Machine Learning Models for Sentiment and Emotion Classification from Vaccine #sideffects

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