Deep Learning-Based Approaches for Sentiment AnalysisBasant Agarwal, Richi Nayak, Namita Mittal, Srikanta Patnaik Springer Nature, 2020 M01 24 - 319 páginas This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.
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Contenido
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Recent Trends and Advances in Deep LearningBased Sentiment Analysis | 32 |
Deep Learning Adaptation with Word Embeddings for Sentiment Analysis on Online Course Reviews | 57 |
Toxic Comment Detection in Online Discussions | 85 |
AspectBased Sentiment Analysis of Financial Headlines and Microblogs | 110 |
Deep LearningBased Frameworks for AspectBased Sentiment Analysis | 139 |
Transfer Learning for Detecting Hateful Sentiments in Code Switched Language | 159 |
Multilingual Sentiment Analysis | 193 |
Sarcasm Detection Using Deep LearningBased Techniques | 237 |
Deep Learning Approaches for Speech Emotion Recognition | 259 |
Bidirectional Long ShortTerm MemoryBased SpatioTemporal in Community Question Answering | 290 |
Comparing Deep Neural Networks to Traditional Models for Sentiment Analysis in Turkish Language | 311 |
Otras ediciones - Ver todas
Deep Learning-Based Approaches for Sentiment Analysis Basant Agarwal,Richi Nayak,Namita Mittal,Srikanta Patnaik Sin vista previa disponible - 2020 |
Deep Learning-Based Approaches for Sentiment Analysis Basant Agarwal,Richi Nayak,Namita Mittal,Srikanta Patnaik Sin vista previa disponible - 2021 |
Términos y frases comunes
ABSA accuracy algorithm annotated answer applications approaches architecture arXiv aspect attention authors better calculated challenge chapter classification combination compared Computational Computational Linguistics considered consists contains context convolutional dataset deep learning dependencies detection discuss distribution document domain emotion recognition English Evaluation example extraction F1 score final function gate given hate speech IEEE important improve input International Conference labels layer Linguistics LSTM machine learning meaning mechanism memory method Natural Language Processing negative neural network obtained opinion output performance phrases polarity positive prediction present problem Proceedings Processing proposed question recurrent representation represents resource samples score selection semantic sentence sentiment analysis sentiment classification sequence shows similar social media speech Systems Table task techniques toxic tweets Twitter various vector weights word embeddings