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Sentiment analysis of multi social media using machine and deep learning models: a review

Multimedia Tools and Applications 2024年第42期83卷 90033-90051页

作者： Vasanthi, P. Madhu Viswanatham, V. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

The Internet has become an important origin of text information, which is then used inside a wide range of research domains. This has been regarded as a necessary foundation for institutions to obtain valuable information in addition to assessing consumers' feelings and views on a particular subject. Message clustering is the method of assigning specific tags to known categories based on one ‘s content material. It is difficult to classify the text using aspect-based sentiment. So each research on sentiment has addressed this issue, as existing research focuses on file and predominant component assessment instead of the specifics of the emotions. This survey reviewed the sentiment analysis based on both X and Facebook. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

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Realizing the potential of Internet of Things (IoT) in Industrial applications

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Discover Internet of Things 2025年第1期5卷 1-16页

作者： Rathi, Bharat Thapaswi, S. Kambhampati, Meghana Jain, Vineet Akshay, P. Pandey, Trilok Nath Pradhan, Sunil Kumar School of Electronics Engineering Vellore Institute of Technology Chennai600127 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

The Internet of Things (IoT), which enables seamless connectivity and effective data exchange between physical items and digital systems, has completely changed the way we interact with our surroundings. This study evaluates IoT applications in healthcare, transportation, agriculture, and smart cities, highlighting advancements, opportunities, and challenges. Using a systematic literature review and case study analysis, it identifies essential IoT components, including sensors, communication protocols, data analytics, and security methods. Key findings demonstrate IoT's potential to enhance healthcare through real-time monitoring and cost reduction, optimize transportation systems for resource efficiency, improve agricultural productivity via precision farming, and enable sustainable urban development in smart cities. However, challenges like data privacy, interoperability, and scalability remain critical barriers to widespread adoption. This study underscores the need for interdisciplinary collaboration among researchers, policymakers, and industry stakeholders to address these challenges and unlock IoT's full potential. It provides actionable insights and future directions for realizing a connected, efficient, and sustainable IoT-driven future. © The Author(s) 2025.

关键词： Precision agriculture

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A Study on the Impact of Sentiment Analysis on Stock Market Prediction

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Recent Advances in computer science and Communications 2023年第1期16卷 73-93页

作者： Dhanasekaren, Kiran Aluri, Sri Teja Karthikeyan, Neeraj Baskaran, Saravanan Hari Selvanambi, Ramani School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Background: Investors estimate how a company's stock or financial instrument will perform in the future, which is known as the stock market prediction. Stock markets are one of the many industries that have benefited substantially from the incredible breakthroughs in machine learning. To effectively estimate these markets, many researchers and companies are continually researching and developing various state-of-the-art approaches and algorithms. Objective: The objective is to predict stock prices based on public sentiments. With a big collection of data from microblogging sites like Twitter, it is possible to analyse the thoughts or feelings of users on a wide scale. These sentiments play a major part in the way the stock market works. We review multiple papers and provide the advantages and disadvantages of various methods. Methods: An in-depth examination of the most recent methodologies for predicting stock market values using sentiment analysis is offered, as well as the multiple consequences for stock markets when epidemics or major events occur. Results: According to the findings, impact sentiment analysis has a significant part in predicting stock market price movement, allowing for greater profit. Conclusion: With modern machine learning and deep learning processes, we can forecast stock costs with a few degrees of precision. This research examines how stock expectations have changed over time, as well as the most recent and effective technique for forecasting, supplying, and minimizing speculators' losses. © 2023 Bentham science Publishers.

关键词： Sentiment analysis

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Classifying distinct emotions from parents of ASD child using EEG source data by combining Bernoulli–Laplace Prior and graph neural networks

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Neural Computing and Applications 2025年第12期37卷 7877-7895页

作者： ArulDass, Stephen Dass Jayagopal, Prabhu School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India

Emotion recognition using biological brain signals needs to be reliable to attain effective signal processing and feature extraction techniques. The impact of emotions in interpretations, conversations, and decision-making, has made automatic emotion recognition and examination of a significant feature in the field of psychiatric disease treatment and cure. The problem arises from the limited spatial resolution of EEG recorders. Predetermined quantities of electroencephalography (EEG) channels are used by existing algorithms, which combine several methods to extract significant data. The major intention of this study was to focus on enhancing the efficiency of recognizing emotions using signals from the brain through an experimental, adaptive selective channel selection approach that recognizes that brain function shows distinctive behaviors that vary from one individual to another individual and from one state of emotions to another. We apply a Bernoulli–Laplace-based Bayesian model to map each emotion from the scalp senses to brain sources to resolve this issue of emotion mapping. The standard low-resolution electromagnetic tomography (sLORETA) technique is employed to instantiate the source signals. We employed a progressive graph convolutional neural network (PG-CNN) to identify the sources of the suggested localization model and the emotional EEG as the main graph nodes. In this study, the proposed framework uses a PG-CNN adjacency matrix to express the connectivity between the EEG source signals and the matrix. Research on an EEG dataset of parents of an ASD (autism spectrum disorder) child has been utilized to investigate the ways of parenting of the child's mother and father. We engage with identifying the personality of parental behaviors when regulating the child and supervising his or her daily activities. These recorded datasets incorporated by the proposed method identify five emotions from brain source modeling, which significantly improves the accurac

关键词： Electroencephalography

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An improved evaporation rate water cycle algorithm for energy-efficient routing protocol in WSNs

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International Journal of Intelligent Computing and Cybernetics 2023年第1期16卷 30-45页

作者： Vimala Dayalan Manikandan Kuppusamy School of Computer Science and Engineering Vellore Institute of TechnologyVelloreIndia

Purpose-The paper aims to introduce an efficient routing algorithm for wireless sensor networks(WSNs).It proposes an improved evaporation rate water cycle(improved ER-WC)algorithm and outlining the systems performance in improving the energy efficiency of *** proposed technique mainly analyzes the clustering problem of WSNs when huge tasks are ***/methodology/approach-This proposed improved ER-WC algorithm is used for analyzing various factors such as network cluster-head(CH)energy,CH location and CH density in improved *** proposed study will solve the energy efficiency and improve network throughput in ***-This proposed work provides optimal clustering method for Fuzzy C-means(FCM)where efficiency is improved in *** evaluations are conducted to find network lifespan,network throughput,total network residual energy and network *** limitations/implications-The proposed improved ER-WC algorithm has some implications when different energy levels of node are used in *** implications-This research work analyzes the nodes’energy and throughput by selecting correct CHs in intra-cluster *** can possibly analyze the factors such as CH location,network CH energy and CH ***/value-This proposed research work proves to be performing better for improving the network throughput and increases energy efficiency for WSNs.

关键词： WSNs Improved ER-WCA Energy efficiency Routing protocol PCM Fuzzy C-Means Paper type Research paper

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Secure Virtual Machine Live Migration using Advanced Metric Encryption

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Recent Advances in computer science and Communications 2023年第6期16卷 16-21页

作者： Narayanan, Gokul Geetha Saravanaguru, Ramaiah Kannan School of Computer Science and engineering Vellore Institute of Technology Vellore India

Cloud is based on the underlying technology of virtualization. Here, the physical servers are divided into multiple virtual servers. Through the technology of virtualization, each virtual server contains virtual machines. Live virtual machine migration is to have migration time and inactive time with minimal duration. Various machine-learning approaches have been investigated and research gaps were identified to enhance the security features during the migration process. Moreover, a secure virtual machine live migration is proposed using Advanced Metric Encryption (AME). Considering the duration of live migration in data centers as well as ensuring the security aspects, the proposed model has been tested and evaluated. © 2023 Bentham science Publishers.

关键词： Virtual machine

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Hybrid deep learning based digital image watermarking using GAN-LSTM and adaptive gannet optimization techniques

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Multimedia Tools and Applications 2024年 1-31页

作者： Shedole, Sambhaji Marutirao Santhi, V. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Katpadi Vellore632014 India

Nowadays, multimedia technology is progressing everyday. It is very easy to duplicate, distribute and modify digital images with online editing software. Image security and privacy are critical aspects of the multimedia revolution. Therefore, digital image watermarking offers an alternative way out for image authentication. Currently, watermarking methods are crucial for safeguarding digital images. Several traditional watermarking approaches have been developed to protect images using spatial domains and transformations. Watermarking techniques that are more traditional are less resistant to repeated attacks. Deep learning-based watermarking has recently gained traction, greatly improving the safety of visual images in a variety of common applications. This study presents a robust and secure digital watermarking method for multimedia content protection and authentication. The watermark image is first transformed using the hybrid wavelet transform, and then it is encrypted using a chaos encryption algorithm. The cover image is simultaneously subjected to neighborhood-based feature extraction. Leveraging these extracted features, a novel Adaptive Gannet Optimization algorithm (AGOA) is employed to determine the optimal embedding location. Subsequently, the watermarked image is seamlessly integrated and extracted using the hybrid Generative adversarial network-based long short-term memory (GAN-LSTM) approach within the identified optimal region. Decryption and Inverse transformation are then used to get the original watermark image. Several previous methods, such as DNN, Deep-ANN, and Deep-CNN, are used to evaluate the performance of the proposed method. This technique improves multimedia content protection and authentication by guaranteeing strong and secure watermarking. The proposed method for digital image watermarking produced a peak signal-to-noise ratio of 46.412 and a mean square error of 24.512. Therefore, the proposed method performs well in digital image wa

关键词： Wavelet transforms

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Enhancing Multicriteria-Based Recommendations by Alleviating Scalability and Sparsity Issues Using Collaborative Denoising Autoencoder

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computers, Materials & Continua 2024年第2期78卷 2269-2286页

作者： S.Abinaya K.Uttej Kumar School of Computer Science&Engineering Vellore Institute of TechnologyChennaiTamilnadu600127India

A Recommender System(RS)is a crucial part of several firms,particularly those involved in *** conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer ***,businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’*** the other hand,the collaborative filtering(CF)algorithm utilizing AutoEncoder(AE)is seen to be effective in identifying user-interested ***,the cost of these computations increases nonlinearly as the number of items and users *** triumph over the issues,a novel expanded stacked autoencoder(ESAE)with Kernel Fuzzy C-Means Clustering(KFCM)technique is proposed with two *** the first phase of offline,the sparse multicriteria rating matrix is smoothened to a complete matrix by predicting the users’intact rating by the ESAE approach and users are clustered using the KFCM *** the next phase of online,the top-N recommendation prediction is made by the ESAE approach involving only the most similar user from multiple *** the ESAE_KFCM model upgrades the prediction accuracy of 98.2%in Top-N recommendation with a minimized recommendation generation *** experimental check on the Yahoo!Movies(YM)movie dataset and TripAdvisor(TA)travel dataset confirmed that the ESAE_KFCM model constantly outperforms conventional RS algorithms on a variety of assessment measures.

关键词： Recommender systems multicriteria rating collaborative filtering sparsity issue scalability issue stacked-autoencoder Kernel Fuzzy C-Means Clustering

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Automatic emotion recognition using deep neural network

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Multimedia Tools and Applications 2025年 1-30页

作者： Sujatha, R. Chatterjee, Jyotir Moy Pathy, Baibhav Hu, Yu-Chen School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of CSE Graphic Era University Dehradun India School of Electrical Engineering Vellore Institute of Technology Vellore India Department of Computer Science Tunghai University Taichung City Taiwan

Emotions are a vital semantic part of human correspondence. Emotions are significant for human correspondence as well as basic for human–computer cooperation. Viable correspondence between people is possibly achieved when both the importance and the emotion of the correspondence are perceived by all groups included. Understanding the significance of language has generally been concentrated on in natural language processing (NLP) as a semantic examination. In NLP, the text can be handled appropriately for classification. Emotion detection from facial emotion is the subfield of social signal processing applied in a wide assortment of regions, explicitly for human and PC collaboration. Many researchers have proposed various approaches, generally utilizing machine learning concepts. Automatic emotion recognition (AER) is significant for working with consistent intuitiveness between a person and a smart device toward fully acknowledging an intelligent society. Many researchers examined cross-lingual and multilingual speech emotion as a stage toward language-free emotion acknowledgment in natural speech. In the present work, we are proposing a deep learning-based AER system using four openly accessible datasets, namely Basic Arabic Vocal Emotions Dataset (BAVED), Acted Emotional Speech Dynamic Database (AESDD), Urdu written in Latin/Roman Script (URDU), and Toronto Emotional Speech Set (TESS), by utilizing the Jupyter notebook and a Python library for music and audio synthesis named Librosa. The experimental results exhibited that the proposed approach achieves better than the existing approaches, i.e., the accuracy of the proposed system with the URDU dataset is 96.24%, the TESS dataset is 99.10%, the AESDD dataset is 65.97%, and the BAVED dataset is 73.12%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantics

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Optimized Neural Network for Automated Cardiovascular Disease Classification: Meta-heuristic Enabled Model

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SN computer science 2025年第1期6卷 1-14页

作者： Kusuma, S. Jothi, K.R. Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Madanapalle India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Cardiovascular disease remains a major issue for mortality and morbidity, making accurate classification crucial. This paper introduces a novel heart disease classification model utilizing Electrocardiogram (ECG) signals. The model consists of two main steps: feature extraction using a refined Lion Algorithm with New Mutated evaluation (LA-NM) and classification through an ensemble approach combined with a Neural Network (NN). The proposed model significantly outperforms traditional classifiers. At 80% training data, it achieves accuracy improvements of 8.4% to 3.11% over Neural Network (NN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and ensemble models. At 90% training data, it shows precision improvements ranging from 1.03% to 0.31% better than the comparison models and specificity improvements ranging from 13.92% to 0.52%. These results underscore the effectiveness of the model and suggest its potential for further development in personalized treatment recommendations based on patient history. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ensemble models Feature extraction Heart disease classification Neural network Optimization

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