Sentiment Analysis: Mining Opinions, Sentiments, and EmotionsCambridge University Press, 15 Okt 2020 - 448 halaman 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. |
Isi
Introduction | 1 |
Document Sentiment Classification | 55 |
Sentence Subjectivity and Sentiment Classification | 89 |
Aspect Sentiment Classification | 115 |
Aspect and Entity Extraction | 168 |
Sentiment Lexicon Generation | 227 |
Analysis of Comparative Opinions | 243 |
Opinion Summarization and Search | 259 |
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adjective algorithm Annual Meeting applications approach aspect expressions aspect extraction aspect-based Association for Computational Bing Liu blog camera Chapter Chen comparative opinions Computational Linguistics conditional random fields Conference on Empirical context corpus data mining detection discussed document-level domain emotion Empirical Methods fake reviews Gibbs sampling identify intent International Conference iPhone knowledge labeled large number lexicon-based LSTM machine learning Methods in Natural n-grams Natural Language Processing negation word negative opinions negative sentiment neural network nodes noun phrases opinion holder opinion targets patterns picture quality positive or negative positive sentiment posts prediction problem Proceedings proposed rules score Section semantic sentence-level sentiment analysis sentiment classification sentiment expressions sentiment lexicon sentiment orientation sentiment words similar social media spammers subjective summary supervised learning syntactic task topic modeling tweets types unigrams userids vector verb Wang word embeddings WordNet Zhang