Sentiment Analysis and Opinion Mining
6,885 citations recorded by the source corpus.
Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.
Subfields: Artificial Intelligence
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Citations in the corpus
- Explainable Recommendation: A Survey and New Perspectives (2018, background): “Liu et al. (2019) extended the idea by considering the fact that ratings in the user-item interaction matrix are missing not at random.”
- Attention-based LSTM for Aspect-level Sentiment Classification (2016, background): “Sentiment analysis (Nasukawa and Yi, 2003), also known as opinion mining (Liu, 2012), is a key NLP task that receives much attention these years.”
- Document Modeling with Gated Recurrent Neural Network for Sentiment Classification (2015, background): “Document level sentiment classification is a fundamental task in sentiment analysis, and is crucial to understand user generated content in social networks or product reviews (Manning and Schütze, 1999; Jurafsky and Martin, 2000; Pang and Lee, 2008; Liu, 2012).”
- Lifelong Learning for Sentiment Classification (2015, methodology): “9,Li et al., 2012,Li et al., 2013,Pan and Yang, 2010,Tan et al., 2007,Wu et al., 2009,Xia and Zong, 2011,Yoshida et al., 2011). Additional details about these and other related works can be found in (Liu, 2012). However, as we discussed in the introduction, these methods do not focus on the ability ”
- Self-Supervised Learning: Generative or Contrastive (2020, background): “DEEP neural networks [71] have shown outstanding performance on various machine learning tasks, especially on supervised learning in computer vision (image classification [26], [48], [54], semantic segmentation [39], [76]), natural language processing (pre-trained language models [27], [68], [75], [”
- 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).”
- Learning on the Job: Online Lifelong and Continual Learning (2020, background): “Sentiment classification (SC) (Liu, 2012) is a good example because different tasks in SC have a lot of knowledge sharing, e.”
- Lifelong Learning CRF for Supervised Aspect Extraction (2017, background): “Aspect extraction is a key task of opinion mining (Liu, 2012).”
- Reducing sentiment polarity for demographic attributes in word embeddings using adversarial learning (2020, background): “The Sentiment Lexicon is an established corpus for positive and negative sentiment word labels used in many works [15, 20, 26].”
- Transformation Networks for Target-Oriented Sentiment Classification (2018, background): “Target-oriented (also mentioned as “target-level” or “aspect-level” in some works) sentiment classification aims to determine sentiment polarities over “opinion targets” that explicitly appear in the sentences (Liu, 2012).”
- End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification (2017, background): “Sentiment classification is an important task in natural language processing and is essential to understand user opinions in social networks or product reviews [Pang and Lee, 2008; Liu, 2012].”
- A survey of sentiment analysis in social media (2018, background): “In [63], the above five components are considered as essential.”
- New classification models for detecting Hate and Violence web content (2015, background): “…use direct indicators of sentiment, i.e. sentiment bearing words; (2) the learning approach - supervised, classification based algorithms, exploit indirect indicators of sentiment that can reflect genre or topic specific sentiment patterns (Pan and Lee, 2008; Liu, 2012; Thelwall et al, 2012).”
- Resolving Complex Cases of Definite Pronouns: The Winograd Schema Challenge (2012, background): “Fine-grained polarity analysis would be a promising solution to this problem (see Pang and Lee (2008) and Liu (2012) for related work).”
- Gender bias in sentiment analysis (2018, background): “There are two main sentiment analysis strategies (Liu, 2012; Pang & Lee, 2008).”