A Survey of Opinion Mining and Sentiment Analysis

By B. Liu, Lei Zhang. 2012

1,528 citations recorded by the source corpus.

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Citations in the corpus

Joint Topic-Semantic-aware Social Recommendation for Online Voting (2017, background): “Such a recommender system can also benefit a variety of other online services such as personalized advertising [26], market research [11], public opinion analysis [17], etc.”
Mining Text Data (2012, methodology): “A number of similar methods along this spirit are proposed in [4, 14, 35, 47, 89] with varying levels of supervision in the clustering process.”
Like It or Not (2016, background): “This is also supported by Liu and Zhang [2012], who reported that manually labeled data can lead to superior results.”
The predictive power of public Twitter sentiment for forecasting cryptocurrency prices (2020, background): “Sentiment analysis or opinion mining is the computational study of people's opinions, appraisals, attitudes, and emotions toward entities, individuals, issues, events, topics and their attributes (Liu and Zhang, 2012) where the main goal is to assign a positive, negative or neutral sentiment polarit”
Do users rate or review?: boost phrase-level sentiment labeling with review-level sentiment classification (2014, background): “(assigning the S for an F-O pair) precisions of around 70% ∼ 80% [4].”
Long Short-Term Memory Over Recursive Structures (2015, background): “The phrase sentiment task includes 318582, 41447, and 82600 phrases for the three sets.”
Connotation Frames: A Data-Driven Investigation (2015, methodology): “We also build upon the extensive amount of literature in sentiment analysis (Pang and Lee, 2008; Liu and Zhang, 2012), especially the recent emerging efforts on implied sentiment analysis (Feng et al.”
The evolution of sentiment analysis - A review of research topics, venues, and top cited papers (2016, background): “The top-cited papers for 2012 and 2013 were both among the top-five cited all time and were already summarized [Cambria et al. 2013; Liu and Zhang 2012].”
SemEval-2016 Task 6: Detecting Stance in Tweets (2016, background): “surveys (Pang and Lee, 2008; Liu and Zhang, 2012; Mohammad, 2015) and proceedings of recent shared task competitions (Wilson et al., 2013; Mohammad et al., 2013; Rosenthal et al., 2015). See Pontiki et al. (2014), Pontiki et al. (2015), and Kiritchenko et”
Explicit factor models for explainable recommendation based on phrase-level sentiment analysis (2014, background): “It is important to notice that the increasingly growing amount of user generated textual reviews contain rich information about product features and user preferences, which can be extracted, summarized and structured by Sentiment Analysis (SA) [18, 24].”
Stance and Sentiment in Tweets (2016, background): “There is a vast amount of work in sentiment analysis of tweets, and we refer the reader to surveys [Pang and Lee 2008; Liu and Zhang 2012; Mohammad 2015] and proceedings of recent shared task competitions [Wilson et al. 2013; Rosenthal et al. 2015].”
How Translation Alters Sentiment (2016, background): “Surveys by Pang and Lee (2008), Liu and Zhang (2012), and Mohammad (2016) give details of many of these approaches.”
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research (2020, background): “Similar trends can be observed in other benchmark datasets such as Yelp, SST, Socher et al. [194], and Amazon Reviews, Zhang et al. [245].”
Aspect Based Sentiment Analysis with Gated Convolutional Networks (2018, background): “Instead of predicting the overall sen-1 timent polarity, ﬁne-grained aspect based sentiment analysis (ABSA) (Liu and Zhang, 2012) is proposed to better understand reviews than traditional sentiment analysis.”
Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks (2021, unknown): “Sentiment analysis and opinion mining [1] have become a hot research topic in natural language processing (NLP) with the rapid development of social media communities.”
Sentiment analysis: A review and comparative analysis of web services (2015, background): “Most of them group the works from the point of view of the different applications/challenges that can be found in SA as in [68,46].”