Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums

By Ahmed Ali Abbasi, Hsinchun Chen, Arab Salem. TOIS, 2008

1,037 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c70ccd66f5d5a247748ccc7de3e31a08d6ab949a

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In Artificial Intelligence

Citations in the corpus

Sentiment in short strength detection informal text (2010, positive): “In terms of classification algorithms, support vector machines (SVMs) are widely used (Abbasi et al., 2008; Abbasi et al., 2008; Argamon et al., 2007; Gamon, 2004; Mishne, 2005; Wilson, Wiebe, & Hwa, 2006) because they seem to perform as well or better than other methods in most machine learning con”
Feature-based transfer learning with real-world applications (2010, background): “In sentiment analysis, there are some interesting tasks, for example, opinion extraction and summarization [81, 118, 184, 93], opinion integration [117], review spam identification [88], review quality prediction [116, 114] and opinion analysis in multiple languages [1], etc.”
Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network (2013, methodology): “Much of the existing research in sentiment analysis examines two major issues: the features utilized to represent the unit of writing (Abbasi et al., 2008), and the techniques applied to perform the sentiment analysis (Pang et al.”
Document-level sentiment classification: An empirical comparison between SVM and ANN (2013, methodology): “Despite the low computational cost of the Naı̈ve Bayes technique, it has not been competitive in terms of classification accuracy when compared to SVM (Abbasi et al., 2008; Pang et al., 2002) and therefore we only analyze the NB method comparatively in the context of our experimental results.”
Author’s Sentiment Prediction (2020, background): “Sentiment analysis solutions have covered a wide range of domains including movie reviews (Pang et al., 2008; Singh et al., 2013; Socher et al., 2011), product reviews (Dave et al., 2003; Turney, 2002; Fang and Zhan, 2015), and social media (Abbasi et al., 2008; Pak and Paroubek, 2010).”
A Lexicon-based Approach for Hate Speech Detection (2015, background): “For instance, in the so called dark web forums, extremists and terrorist groups communicate, share ideologies, and use the forums as conduits for radicalization [1].”
News impact on stock price return via sentiment analysis (2014, methodology): “[1] use sentiment analysis to construct sentiment features (both stylistic and syntactic).”
How Translation Alters Sentiment (2016, background): “Abbasi, Chen, and Salem (2008) deployed Arabic morphological, syntactic and stylistic features for sentiment analysis of Arabic web forums.”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, background): “S# Tasks and applications #Articles References 1 Subjectivity Classification 6 [44], [75], [110], [163], [167], [174] 2 Polarity determination 43 [12], [26], [29], [32], [33], [35], [40], [45], [48], [50], [54], [57], [66], [85], [95], [96], [108], [109], [112], [114], [123], [126], [154], [156], [1”
Weakly Supervised Joint Sentiment-Topic Detection from Text (2012, background): “, from manually built appraisal groups [5], to semiautomatically [16] or fully automatically [17] constructed lexicons.”