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 |
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ABSA accuracy algorithm annotated answer arXiv preprint aspect aspect-based sentiment analysis aspect-term Association for Computational attention mechanism autoencoders capture challenge code mixed code switched Computational Linguistics Conference on Empirical context convolutional neural network dataset deep learning deep learning approaches deep learning-based deep neural network discuss document domain encoded Evaluation FastText feature extraction fine-grained hate speech hate speech detection Hinglish IEEE input International Conference labels layer long short-term memory low resource languages LSTM model machine learning Methods in Natural n-grams Natural Language Processing negative opinion mining output parameters phrases polarity pre-trained prediction problem Proceedings proposed random forest recurrent neural networks sarcasm semantic sentence sentiment analysis sentiment classification sentiment score sequence social media softmax speech emotion recognition Springer ST-biLSTM support vector machines target task techniques training data transfer learning tweets Twitter Wang word embeddings word vectors Word2Vec Workshop Zhang