Open AccessBook
Sentiment Analysis: Mining Opinions, Sentiments, and Emotions
TLDR
Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes as discussed by the authors, which offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis.Abstract:
Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences.In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and also new material on emotion and mood analysis techniques, emotion-enhanced dialogues, and multimodal emotion analysis.read more
Citations
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Journal ArticleDOI
Sentiment of Emojis.
TL;DR: The first emoji sentiment lexicon is provided, called the Emoji Sentiment Ranking, and a sentiment map of the 751 most frequently used emojis is drawn, which indicates that most of the emoji are positive, especially the most popular ones.
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Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN
TL;DR: A divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type, which shows that sentence type classification can improve the performance of sentence-level sentiment analysis.
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Bidirectional LSTM with attention mechanism and convolutional layer for text classification
Gang Liu,Jiabao Guo +1 more
TL;DR: A novel and unified architecture which contains a bidirectional LSTM (BiLSTM), attention mechanism and the convolutional layer is proposed in this paper, which outperforms other state-of-the-art text classification methods in terms of the classification accuracy.
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Enhancing deep learning sentiment analysis with ensemble techniques in social applications
TL;DR: This paper develops a deep learning based sentiment classifier using a word embeddings model and a linear machine learning algorithm and proposes two ensemble techniques which aggregate this baseline classifier with other surface classifiers widely used in Sentiment Analysis.
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Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review
TL;DR: This article aims to provide a comparative review of deep learning for aspect-based sentiment analysis to place different approaches in context.
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