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检索条件"任意字段=3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024"

共 1222 条记录，以下是1-10 订阅

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Proceedings - 2024 3rd international conference on sentiment analysis and deep learning, icsadl 2024

Proceedings - 2024 3rd International Conference on Sentiment...

引用

3rd international conference on sentiment analysis and deep learning, icsadl 2024

ISBN: (纸本)9798350396157

The proceedings contain 121 papers. The topics discussed include: ***: a cutting-edge solution for informed meal selection;a hybrid learning model for detecting attacks in cloud computing;a real time prediction model for round winners of counter strike: global offensive;social media censoring using of visual sentimental analyzer;a comparative analysis of machine learning and deep learning algorithms for skin cancer detection and classification: exploring performance across multiple parameters;an extensive review of intelligent deep learning based anomaly detection and classification of 3D MRI brain imaging data;evaluating human emotions using image processing and brain waves;and hybrid machine learning based shill bidding fraud detection system using recursive feature elimination method.

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sentiment analysis of Movie Reviews Using deep learning 24

Sentiment Analysis of Movie Reviews Using Deep Learning

引用

3rd international conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Liu, Minghao Faculty of Computer Science and Technology Qilu University of Technology Shandong Jinan China

ISBN: (纸本)9798400710834

The rapid proliferation of mobile devices has driven the integration of artificial intelligence (AI) technologies, making the efficient operation of complex deep learning models within resource-limited environments a critical challenge in AI. This study addresses this challenge by focusing on mobile applications and proposing a streamlined sentiment analysis model specifically tailored for English movie reviews. We begin by exploring the diverse applications of sentiment analysis in user feedback and public opinion monitoring, underscoring its growing significance. Subsequently, we provide an overview of the evolving landscape of related technologies. In our experiment, we utilize the bag-of-words model for data preprocessing, which we combine with a convolutional neural network (CNN) and dropout regularization to construct a lightweight model architecture. Through iterative parameter optimization, we successfully achieve a delicate balance between model performance and complexity. We trained the model on a dataset consisting of 20,000 samples and achieved an accuracy of 81.76% on a test set of 5,000 samples. Compared to traditional, more complex sentiment analysis models, our proposed approach significantly reduces computational demands, demonstrating its potential for practical deployment on mobile devices. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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sentiment Detection in Financial News: A deep learning Approach to Extreme and Moderate Classification 3

Sentiment Detection in Financial News: A Deep learning Appro...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Didolkar, Amey Lokulwar, Prasad G H Raisoni College of Engineering Computer Science and Engineering Nagpur India

ISBN: (纸本)9798331515911

Today's financial industries need precise, directed news analysis with sentiment identification more than ever in order to forecast possible future moves. This research focuses on developing a robust system for sentiment detection in financial news using a deep learning approach, enabling both extreme and moderate. The data collection has been ensured through the use of web scraping to gather data from multiple reliable financial news sources. To reliably forecast the attitudes encoded in the news items, this dataset has been processed using state-of-the-art deep learning algorithms. The latest technologies in deep learning have been employed, and a comparative analysis of different models has been conducted to identify the most effective one of this specific task. Providing an easy-to-use interface that allows users to obtain news from many sources and their corresponding sentiment analysis is the main goal of this research. With the help of this technology, stakeholders should be able to make well-informed decisions by using the sentiment trends found in financial news. By providing the effectiveness of deep learning models in sentiment identification, the system not only improves user experience by offering sentiment-based news insights but also advances the field of financial news analysis. The deep learning model that was constructed has demonstrated encouraging results in reliably classifying attitudes through extensive testing and evaluations, indicating that it has the potential to be an instrumental tool for financial analysis. For future research in the fields of sentiment identification and financial forecasting, this study establishes a precedent and emphasis the significance of incorporating cutting-edge technologies into news analysis. © 2025 IEEE.

关键词： Contrastive learning

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A Novel Generative AI-Powered Approach for sentiment analysis: Enhancing deep learning Models with Contextual Understanding 3

A Novel Generative AI-Powered Approach for Sentiment Analysi...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Naik, Mahendra Shridhar Chaitra, S.N. Shashikala, K.S. New Horizon College of Engineering Department of Electronics & Communication Bengaluru India Gm Institute of Technology Department of Electronics & Communication Davangere India New Horizon College of Engineering Department of Artificial Intelligence & Machine Learning Bengaluru India

ISBN: (纸本)9798331515911

sentiment analysis, a pivotal aspect of natural language processing (NLP), has seen notable advancements with the advent of deep learning models. Despite these advancements, challenges persist in accurately capturing nuanced emotions and understanding context-dependent sentiments. This paper introduces an innovative method that integrates generative adversarial networks (GANs) into deep learning architectures to enhance sentiment analysis. By leveraging GAN's ability to generate contextually relevant text, the proposed approach enriches the training data, thereby improving the model's ability to discern subtle sentiment variations. Our extensive experiments on benchmark datasets reveal that our method surpasses traditional deep learning models, achieving a 5% improvement in accuracy, a 7% higher F1-score, and demonstrating superior robustness to sarcasm and irony. © 2025 IEEE.

关键词： Generative adversarial networks

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sentiment Detection Using X-NLP: Harnessing Advanced NLP Techniques 3

Sentiment Detection Using X-NLP: Harnessing Advanced NLP Tec...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Vaneeta, M. Pandey, Aryan Ravi, A. Mahammadinthiyaz Ashwath Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9798331515911

sentiment analysis is a useful way to gain insights from text, helping organizations make informed decisions based on the emotions in the information they collect. This project aims to create a sentiment analysis system using Explainable Natural Language Processing (X-NLP) to clarify how sentiment predictions are made. The system offers users a look into how certain words and features affect sentiment classification. By using explainable AI techniques, it ensures that users not only get accurate sentiment labels but also understand why these labels were assigned, building trust and ease of use. The models are trained on the Sp1786 database and a larger data-set of 1.6 million tweets, applying both statistical methods (SVM) and deep learning techniques (LSTM). The SVM model achieved a precision of 0.6753, an AUC of 0.8432, and an F1 score of 0.6769, making it effective for real-world use. © 2025 IEEE.

关键词： deep learning

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3rd international conference on Language Processing and Knowledge Management, LPKM 2024

3rd International Conference on Language Processing and Know...

引用

3rd international conference on Language Processing and Knowledge Management, LPKM 2024

ISBN: (纸本)9783031850660

The proceedings contain 29 papers. The special focus in this conference is on Language Processing and Knowledge Management. The topics include: Machine learning Serving the School Orientation Process;improving Arabic Fake News Detection Across Context-Aware Attention deep Model Based on Natural Language Processing;towards a Maude-Based Approach for Formal Modeling deep Neural Networks;Anti-pattern Based IoRT-Aware Business Process Structure Verification Approach;opinion analysis Based on a sentiment Lexical Ontology and deep learning Models: Tunisian Dialect Case;disfluent-to-Fluent Tunisian Dialect Speech Translation with Fine-Tuning Pre-trained Language Models;BERT-Based Model for Sarcasm Detection in Arabic Texts;typology of Event Data Imperfections;towards sentiment analysis for Libyan Dialect;text Categorization Can Enhance Domain-Agnostic Stopword Extraction;normalized Orthography for Tunisian Arabic;deep learning Approach for Early Prediction of Depression on Social Network;adapting Large Language Models to Biomedical Domain: A Survey of Techniques and Approaches;tunisian Arabic Understanding: Resources analysis and Evaluation;from Data to Decisions: An Ontology-Driven Method for Opinion Mining;LLMs for Cyberbullying Detection in Political Social Media;Assessing BERT Models for Arabic Named Entity Recognition in a Multi-dialectal Context;tunisian Normalized Pronunciation;traffScOnto: Ontology for Traffic Management in the Context of Smart City Domain;SERTUS Dataset Collection from Spontaneous Environments;an Agricultural sentiment Dataset for Pest Control and Crop Diseases;deep learning Approach to Identify and Classify Arabic Verbal Multi-word Expressions;a Design Pattern-Based Approach for Analyzing MapReduce Applications;analyzing the Impact of Big Data in Mental Health;The Impact of AI on Knowledge Management;a Rule-Based System for Translating Libyan Dialect Dual Forms to Modern Standard Arabic.

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deep learning Based Predictive Model to Capture Consumer Emotion from the Ecommerce Sector using sentiment analysis Technique 3

Deep Learning Based Predictive Model to Capture Consumer Emo...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Hegde, Sandeep Kumar Hegde, Rajalaxmi Rajeshwari, M.S. Nmam Institute of Technology Department of Cse Karnataka Nitte574110 India

ISBN: (纸本)9798331515911

sentiment analysis has become a popular topic of research in the last several years, and a lot of work has already been done in this area, including sentiment analysis of product reviews on Amazon, Flipkart, and other platforms. Twitter is a multifaceted platform that spreads its wide-ranging dialogue across a variety of industries, such as consumer goods, politics, and education. One way to evaluate the enormous volume of views on Twitter is using sentiment analysis. Nonetheless, recent advancements in sentiment analysis techniques, particularly in relation to Recurrent Neural Networks, have shown a great deal of promise for raising the effectiveness and accuracy of sentiment analysis for Amazon product reviews. In recent years, sentiment analysis has grown in popularity as a tool for analysing product reviews on websites such as ***. The speed and accuracy of sentiment analysis for Amazon product evaluations have improved dramatically with the advancement of methodologies, particularly the use of Recurrent Neural Networks. In the proposed technique, a CNN-based LSTM model was employed to perform sentimental analysis on Amazon product reviews. The experimental findings showed that the suggested CNN-based LSTM model outperformed previous models, with an accuracy rate of 94%. © 2025 IEEE.

关键词： Tweets

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Multimodal sentiment analysis using a Hybrid deep learning Model with Advanced Feature Fusion and Attention Mechanism 3

Multimodal Sentiment Analysis using a Hybrid Deep Learning M...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kuruva, Arjun Nagaraju, C. A.P. Proddatur India

ISBN: (纸本)9798331527549

Multimodal sentiment analysis plays a crucial role in natural language processing by incorporating visual features, such as emojis, to interpret sentiment and mood in text. This paper addresses the challenge of effectively integrating textual and visual modalities, particularly the gap in leveraging emoji data for emotion detection. We propose a hybrid model that combines BERT-based textual embeddings with pre-trained emoji embeddings, advanced feature fusion, and attention mechanisms to enhance sentiment understanding. The attention mechanism enables the model to focus on relevant semantic and emotional cues, while perceptual segmentation allows for improved interpretation of long-term dependencies between text and emojis. When evaluated on the sentiment140 dataset, the proposed model achieves state-of-the-art performance, reaching an accuracy of 92.8%, F1-score of 92.3%, and AUC of 96.2%, outperforming benchmarks like BERT-LSTM and RoBERTa-GRU. These results underscore the effectiveness of advanced feature fusion and attention mechanisms in multimodal sentiment analysis, providing robust emotion detection in social media data. Despite computational challenges, the model demonstrates strong generalization across different data types, offering valuable insights for future research in multimodal emotion analysis. © 2025 IEEE.

关键词： Semantics

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

3rd International Conference on Intelligent Data Communicati...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

ISBN: (纸本)9798331527549

The proceedings contain 354 papers. The topics discussed include: quantifying effectiveness of governmental agriculture policies using RNN-transformer based sentiment analysis;artificial intelligence to enhance the security of internet of things: a comprehensive review;dual-plate verification system for vehicle identification using IoT and embedded systems;eco-friendly next-gen air conditioning with IoT-based embedded monitoring and control for reduced energy consumption;investigation on attack detection in IoT networks: a study and analysis of the existing machine learning and deep learning techniques;monitoring and leak detection of hazardous gas using internet of things;and navigating SAP ERP implementation: identifying success drivers and pitfalls.

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ADMIT 2024 - conference Proceedings: 2024 3rd international conference on Algorithms, Data Mining, and Information Technology

ADMIT 2024 - Conference Proceedings: 2024 3rd International ...

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3rd international conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

ISBN: (纸本)9798400718120

The proceedings contain 70 papers. The topics discussed include: research on the optimization of driving strategy of intelligent networked vehicles based on big data technology;a tennis momentum analysis method based on Gaussian dynamics and machine learning;sentiment and user churn analysis for online platform business using yelp studies;research on commercial vehicle replacement prediction model based on improved attention mechanism;cross-sectional industrialization factors’ contribution to heat wave risk classification;cross-domain person search data augmentation based on style transfer;recurrent convolutional fact graph neural networks for temporal knowledge graph reasoning;and research on network quality evaluation system based on the entropy weight method.

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