Opinion Mining and Sentiment Analysis

By Charu C. Aggarwal. 2018

3,879 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function (2019, background): “It has a wide range of applications such as sentiment analysis (Pang and Lee 2008), topic categorization (Lewis et al. 2004), and email ﬁltering (Sahami et al. 1998).”
Towards Understanding and Mitigating Social Biases in Language Models (2021, background): “Sentiment scores capture differences in overall language polarity (Pang and Lee, 2008), while regard measures language polarity and social perceptions of a demographic (see Sheng et al. (2019) for differences).”
FastText.zip: Compressing text classification models (2016, background): “Text classiﬁcation is a problem that has its roots in many applications such as web search, information retrieval and document classiﬁcation (Deerwester et al., 1990; Pang & Lee, 2008).”
Bag of Tricks for Efficient Text Classification (2016, background): “Text classiﬁcation is an important task in Natural Language Processing with many applications, such as web search, information retrieval, ranking and document classiﬁcation (Deerwester et al., 1990; Pang and Lee, 2008).”
Dynabench: Rethinking Benchmarking in NLP (2021, background): “sition that every text conveys sentiment information (Pang and Lee, 2008).”
Ex Machina: Personal Attacks Seen at Scale (2016, methodology): “Much of this work builds on existing machine learning approaches in fields like sentiment analysis [16] and spam detection [22].”
Hierarchical Attention Networks for Document Classification (2016, background): “It has broad applications including topic labeling (Wang and Manning, 2012), sentiment classiﬁcation (Maas et al., 2011; Pang and Lee, 2008), and spam detection (Sahami et al., 1998).”
Decision support from financial disclosures with deep neural networks and transfer learning (2017, methodology): “For this mapping operation, one primarily follows the bag-of-words approach [13, 14], which we detail in the following.”
Sentence-State LSTM for Text Representation (2018, neutral): “321 Dataset Training Development Test #sent #words #sent #words #sent #words Movie review (Pang and Lee, 2008) 8527 201137 1066 25026 1066 25260 Books 1400 297K 200 59K 400 68K Electronics 1398 924K 200 184K 400 224K DVD 1400 1,587K 200 317K 400 404K Kitchen 1400 769K 200 153K 400 195K Apparel 1400 ”
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation (2017, background): “…on language use (e.g. Hovy & Søgaard, 2015; Johannsen et al., 2015); the prevalence of paralinguistic features such as affect, irony and humour (Pang & Lee, 2008; Lukin & Walker, 2013); and other variables such as personality (e.g. Oberlander & Nowson, 2006; Farnadi et al., 2013; Schwartz et…”
rnn : Recurrent Library for Torch (2015, methodology): “For example if we want to do sentiment analysis [12], we could use something like the following :”
DynaSent: A Dynamic Benchmark for Sentiment Analysis (2020, background): “Rather than trying to survey the ﬁeld (see Pang and Lee 2008; Liu 2012; Grimes 2014), we focus on the benchmark tasks that have emerged in this space, and then seek to situate these benchmarks with respect to challenge (adversarial) datasets and crowdsourcing methods.”
Forward and Backward Knowledge Transfer for Sentiment Classification (2019, background): “SC classiﬁes an opinion document (e.g., a product review) as expressing a positive or negative sentiment (Pang et al., 2008; Cambria et al., 2017).”
Learning low dimensional word based linear classifiers using Data Shared Adaptive Bootstrap Aggregated Lasso with application to IMDb data (2018, background): “This template example can serve as a prototype of a large number of similar problems of sentiment analysis in different areas ( [27], [22], [17], [21]).”
A survey of multimodal sentiment analysis (2017, methodology): “These approaches calculate features over bag-of-words models of the reviews, for which a more detailed review can be found in Pang and Lee [5].”
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].”
Projecting Embeddings for Domain Adaption: Joint Modeling of Sentiment Analysis in Diverse Domains (2018, background): “This leads to classifiers that, after training, perform poorly on new domains (Pang and Lee, 2008; Deriu et al., 2017).”
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.”