Sentiment analysis: capturing favorability using natural language processing

By Tetsuya Nasukawa, Jeonghee Yi. 2003

1,365 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

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.”
Creating Subjective and Objective Sentence Classifiers from Unannotated Texts (2005, methodology): “Automatic subjectivity/opinion/sentiment analysis is being applied to many interesting applications, including classification of reviews [19, 14, 11, 25, 26], analysis of product reputations [26, 25, 27], tracking sentiments toward events [28, 22, 29], and incorporating opinions into into question a”
Identifying comparative sentences in text documents (2006, background): “Other related works on sentiment classification and opinions discovery include [9, 15, 16, 23, 27, 33, 34, 35].”
Large Scale Sentiment Analysis on Twitter with Spark (2016, background): “Moreover, user-generated content that captures sentiment information has proved to be valuable among many internet applications and information systems, such as search engines or recommendation systems.”
Approaches, Tools and Applications for Sentiment Analysis Implementation (2015, background): “[10], sentiments of words [11], subjective sentences [12], and topics [4; 13; 14).”
Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena (2009, methodology): “NLP methods have been used to extract sentiment and opinion from texts such as camera [16] and pharmaceutical reviews [25].”
Which Side are You on? Identifying Perspectives at the Document and Sentence Levels (2006, background): “, 2003), and discriminating between positive and negative language (Pang et al., 2002; Morinaga et al., 2002; Yu and Hatzivassiloglou, 2003; Turney and Littman, 2003; Dave et al., 2003; Nasukawa and Yi, 2003; Popescu and Etzioni, 2005; Wilson et al., 2005).”
Survey on Aspect-Level Sentiment Analysis (2016, methodology): “syntax-based Nasukawa & Yi (2003) [68] general & binary combined (general/camera) precision: 94.”
Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges (2023, background): “Nasukawa and Yi [3] created a sentiment lexicon with 3,513 sentiment terms to determine subject favorability.”
Sentiment Analysis and Opinion Mining (2012, background): “In Neviarouskaya et al. (2010), six types of composition rules were introduced, i.”
Determining the semantic orientation of terms through gloss classification (2005, methodology): “The method we present outperforms all known methods when tested on the recognized standard benchmarks for this task.”
Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis (2004, background): “Pang et al. (2002) have demonstrated, however, that this is not as straightforward as one may think, given that sentiment is often expressed in more subtle and indirect ways.”
Social media big data analytics: A survey (2019, neutral): “Sentiment analysis can be de ﬁ ned as a process of determining favorable or unfavorable opinions toward a speci ﬁ c subject in a texts (Nasukawa & Yi, 2003).”
Just How Mad Are You? Finding Strong and Weak Opinion Clauses (2004, background): “Others perform automatic analyses of product reputations (Morinaga et al., 2002; Nasukawa and Yi, 2003; Yi et al., 2003).”