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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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 bidirectional capture challenge code mixed code switched Computational Linguistics Conference on Empirical context convolutional neural network corpus dataset deep learning deep learning approaches deep learning-based discuss document domain encoded Evaluation F1 score 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 sarcasm semantic sentence sentiment analysis sentiment classification sentiment score sequence social media softmax speech emotion recognition Springer ST-biLSTM support vector machines Systems target task techniques training data transfer learning tweets Twitter Wang word embeddings word vectors Word2Vec Workshop Zhang