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All Journal International Journal of Advances in Applied Sciences Indonesian Journal of Electrical Engineering and Computer Science
Singh, Vaishali
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Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Hybrid model for sentiment analysis combination of PSO, genetic algorithm and voting classification Srivastava, Garima; Singh, Vaishali; Kumar, Sachin
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1151-1161

Abstract

As social network services like Weibo and Twitter have grown in popularity, natural language processing (NLP) has seen a great deal of interest in sentiment analysis of social media messages and Information mining. Social media users, whose numbers are always increasing, have the ability to exchange information on their platforms. The study of sentiment, domains and themes are closely related. Manually collecting enough labelled data from the vast array of subjects covered by large-scale social media to train sentiment classifiers across several domains would be extremely difficult. The literature review conducted concludes that models already proposed in the previous researches are not able to achieve good accuracy. This work suggests a unique model that combines of genetic algorithm and particle swarm optimization to effectively extract the features and then the voting technique is applied for the classification. Model proposed is compared with 4 ensemble datasets achieving a consistent accuracy of more than 90% for three different diversified database owing to natural selection of sequences by GA and at the same time achieves a fast convergence with PSO, the model may be employed for highly accurate recommenders demanding precision and accuracy.
AI-driven emotion recognition systems for sustainable mental health care: an engineering perspective Ahmad, Akram; Singh, Vaishali; Upreti, Kamal
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1111-1117

Abstract

Emotion recognition systems are transforming human-computer interaction (HCI) applications by enabling AI-driven, adaptive, and responsive mental health interventions. This study explores AI-based emotion recognition technologies using facial expressions, voice analysis, text-based sentiment processing, and physiological signals to develop scalable, real-time mental health support systems. Utilizing datasets such as FER2013, JAFFE, and CK+, our research examines deep learning models, including EfficientNet XGBoost, which achieved over 90% accuracy across key evaluation metrics. Unlike traditional mental health interventions, AI-driven systems provide cost-effective, accessible, and sustainable solutions through telemedicine, wearable biosensors, and virtual counselors. The study also highlights critical challenges such as algorithmic bias, ethical AI compliance, and the energy consumption of deep learning models. By integrating machine learning, cloud-based deployment, and edge computing, this research contributes to the development of sustainable, ethical, and user-centric AI solutions for mental health care. Future directions include AI model optimization for energy-efficient deployments and the creation of diverse, inclusive datasets to improve performance across global populations.
Co-Authors Ahmad, Akram Sachin Kumar Srivastava, Garima Upreti, Kamal