Opinion Mining and Sentiment Analysis

By Bo Pang, Lillian Lee. Foundations and Trends in Information Retrieval, 2008

4,940 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b25663fa149be5286de193c13324098aedd7e2cc

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Citations in the corpus

Are They Different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text (2014, background): “In Pang and Lee's work [2], an opinion " implies a conclusion thought out yet open to dispute " ; that is, experts on a topic can have very different opinions, which they might change in light of new evidence.”
Predicting the Future with Social Media (2010, methodology): “Sentiment analysis is a well-studied problem in linguistics and machine learning, with different classifiers and language models employed in earlier work [13], [14].”
Interactive Attention Networks for Aspect-Level Sentiment Classification (2017, background): “Aspect-level sentiment classiﬁcation is a ﬁne-grained task in sentiment analysis which aims to identify the sentiment polarity of targets in their context [ Pang and Lee, 2008; Liu, 2012 ] .”
Does ‘bigger’ mean ‘better’? Pitfalls and shortcuts associated with big data for social research (2015, background): “…Allocation) that allows to automatically individuate on probabilistic basis the topics contained into a complex corpus of texts; the latter is Pang and Lee (2008), about sentiment analysis, a way to automatically calculate partisanship of texts about a certain topic. people expressing…”
Book Reviews: Data-Intensive Text Processing with MapReduce by Jimmy Lin and Chris Dyer (2010, background): “Another example is to automatically situate texts along a scale of “happiness”, a task known as sentiment analysis or opinion mining [118], which has been applied to everything from understanding political discourse in the blogosphere to predicting the movement of stock prices.”
How Happiness Affects Choice (2011, neutral): “The unobtrusive nature of this method thus provides a window into people’s naturally generated thoughts and feelings (Cohn, Mehl, and Pennebaker 2004; Pang and Lee 2008).”
Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak (2020, background): “Sentiment analysis can be helpful in determining the positive or negative polarity of words, phrases, or documents whereby, positive polarity explains favorable sentiments and negative polarity determines unfavorable sentiments towards specific events (Turney and Littman, 2003; Pang and Lee, 2008).”
Recent automatic text summarization techniques: a survey (2016, background): “In such summaries opinions are initially detected and classified on the basis of subjectivity (whether the sentence is subjective or objective) and then on the basis of polarity (positive, negative or neutral) (Pang and Lee 2008).”
Learning to Identify Review Spam (2011, background): “However, their assumption that duplicated review is spam, is not appropriate [Pang and Lee, 2008].”
Towards a Big Data Analytics Framework for IoT and Smart City Applications (2015, methodology): “Most of the complex semantic analysis tasks, as for instance Sentiment Analysis [ 34] [ 45] require batch modeling.”
Twitter mood predicts the stock market (2010, methodology): “In addition, we extract a time series of daily DJIA closing-values from Yahoo!”
Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach (2020, background): “Sentiment analysis (also, known as, opinion mining) is the computational field concerned with contextually mining unstructured text documents so that subjective information (such as opinions, sentiments, attitudes, evaluations, or emotions) can be extracted [ 54].”
Adversarial Distillation for Efficient Recommendation with External Knowledge (2018, background): “The review prediction network maps a piece of user review into a real-value rating, which is similar to the task of sentiment analysis (SA) [36].”