Deep Learning for Depression Detection Using Twitter Data

Intelligent Automation & Soft Computing, Journal Year: 2023, Volume and Issue: 36(2), P. 1301 - 1313

Published: Jan. 1, 2023

Today social media became a communication line among people to share their happiness, sadness, and anger with end-users. It is necessary know people’s emotions are very important identify depressed from messages. Early depression detection helps save lives other dangerous mental diseases. There many intelligent algorithms for predicting high accuracy, but they lack the definition of such cases. Several machine learning methods help people. But accuracy existing was not satisfactory. To overcome this issue, deep method used in proposed detection. In paper, novel Deep Learning Multi-Aspect Depression Detection Hierarchical Attention Network (MDHAN) classifying data. Initially, Twitter data preprocessed by tokenization, punctuation mark removal, stop word stemming, lemmatization. The Adaptive Particle grey Wolf optimization feature selection. MDHAN classifies predicts non-depressed users. Finally, compared as Convolutional Neural (CNN), Support Vector Machine (SVM), Minimum Description Length (MDL), MDHAN. suggested MDH-PWO architecture gains 99.86% more significant than frequency-based models, lower false-positive rate. experimental result shows that achieves better precision, recall, F1-measure. also minimizes execution time.

Language: Английский

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Textual emotion recognition method based on ALBERT-BiLSTM model and SVM-NB classification

Soft Computing, Journal Year: 2023, Volume and Issue: 27(8), P. 5063 - 5075

Published: Feb. 28, 2023

Language: Английский

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Enhancing speech emotion recognition through deep learning and handcrafted feature fusion

Applied Acoustics, Journal Year: 2024, Volume and Issue: 222, P. 110070 - 110070

Published: May 14, 2024

Language: Английский

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A Parallel-Model Speech Emotion Recognition Network Based on Feature Clustering

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 71224 - 71234

Published: Jan. 1, 2023

Speech Emotion Recognition (SER) is a common aspect of human-computer interaction and has significant applications in fields such as healthcare, education, elder care. Although researchers have made progress speech emotion feature extraction model identification, they struggled to create an SER system with satisfactory recognition accuracy. To address this issue, we proposed novel algorithm called F-Emotion select features established parallel deep learning recognize different types emotions. We first extracted from calculated the value for each feature. These values were then used determine combination that was optimal recognition. Next, input train test type emotion. Finally, decision fusion applied output results obtain overall result. analyses conducted on two datasets, RAVDESS EMO-DB, accuracy reaching 82.3% 88.8%, respectively. The demonstrate can effectively analyze correspondence between types.The MFCC best describes emotions Neutral, Happiness, Fear Surprise, Mel Angry Sadness.The mechanism improve

Language: Английский

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Unveiling Emotions from Audio: A Multi-model Exploration Leveraging Diverse Datasets

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 246 - 260

Published: Jan. 1, 2025

Language: Английский

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