Book Review: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions by Bing Liu

By Jun Zhao, Kang Liu, Liheng Xu. 2016

707 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

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Citations in the corpus

Continual Learning with Knowledge Transfer for Sentiment Classification (2021, background): “This presents two key challenges: (1) how to transfer the knowledge learned from the previous tasks to the new task to help it learn better without using the previous tasks’ data, and (2) how to improve old models for the previous tasks in the process without CF? In [15], the authors showed that CL ”
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis (2019, background): “Reviews also serve as a rich resource for sentiment analysis (Pang et al., 2002; Hu and Liu, 2004; Liu, 2012, 2015).”
Big Data and Data Science Methods for Management Research (2016, background): “Newly introduced approaches such as deep learning (LeCun, Bengio & Hinton, 2015) can also help overcome the shortcomings of LDA (Liu, 2015, pp. 169-171).”
Are Emojis Creating a New or Old Visual Language for New Generations? A Socio-semiotic Study (2016, background): “…understanding emojisis that sentiment analysis and opinion mining are important new areas in the field of language processing and as such are positively correlated to both the increased use of emojis but also their place as a new or evolved element of language and communication (Liu, 2012, 2015).”
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research (2020, background): “A detailed review for most of these works has been provided in Liu [119], [120].”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, methodology): “Sentiment analysis systems have been applied to many different kinds of texts including customer reviews (Pang & Lee, 2008; Liu & Zhang, 2012; Liu, 2015), news paper headlines (Bellegarda, 2010), novels (Boucouvalas, 2002; John et al.”
Deep learning for sentiment analysis: A survey (2018, background): “1 For example, in the sentence “ John hates his car ”, the opinion holder is “ john ”.”
Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review (2019, background): “(2016); 4 tags according to Stanford Tagger in Liu et al. (2015), Ye, Yan, Luo, and Chao (2017); or even 45 tag based on Penn Tagger (Jebbara & Cimiano, 2017; Zainuddin, Selamat, & Ibrahim, 2018).”
Stance and Sentiment in Tweets (2016, methodology): “egative language tend to have sufﬁcient amount of commonality regardless of topic of discussion, and hence sentiment analysis systems traditionally learn a single model from all of the training data [Liu 2015; Kiritchenko et al. 2014a; Rosenthal et al. 2015]. Thus, our sentiment experiments are also”
Modeling Empathy and Distress in Reaction to News Stories (2018, background): “Undoubtedly, major progress has been made in NLP, with sentiment analysis being one of the most vivid and productive areas in recent years (Liu, 2015).”
Deep Learning for User Comment Moderation (2017, methodology): “(19)Sentiment features have been used by several methods, but sentiment analysis (Pang and Lee, 2008; Liu, 2015) is typically not directly concerned with abusive content.”
Current State of Text Sentiment Analysis from Opinion to Emotion Mining (2017, background): “Document-level polarity classiﬁcation concerns most of the body of the work for this area and is considered the simplest sentiment analysis task in the research community [Liu 2015].”
Sentiment of Emojis (2015, background): “It is one of the most active research areas in natural-language processing and is also extensively studied in data mining, web mining, and text mining [3, 4].”
Bidirectional LSTM with attention mechanism and convolutional layer for text classification (2019, background): “Sentiment analysis, is a branch of text clas-siﬁcation, which is the ﬁeld of study that analyzes people’s opinions, sentiments, appraisals, attitudes, and emotions toward entities and their attributes expressed in written text [3] .”