Sentiment in short strength detection informal text

By Mike Thelwall, Kevan Buckley, Georgios Paltoglou, Di Cai, Arvid Kappas. 2010

972 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/7b753e096f6dfe61f397bd85e83f63d9dda23af1

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Citations in the corpus

Social innovation research: An emerging area of innovation studies? (2016, neutral): “…practice in bibliometric studies to construct a relaionship between cited references or topic keywords, a method lso known as ‘scientific mapping’ (Boyack and Klavans, 2010). he co-occurrence of keywords or citations (co-citations) within document, respectively link topics or cited articles,…”
New classification models for detecting Hate and Violence web content (2015, background): “SentiStrength (Thelwall et al, 2010, 2012) takes a lexical approach to sentiment analysis, making use of a combination of sentiment lexical resources, semantic rules, heuristic rules and additional rules.”
A Survey on Hate Speech Detection using Natural Language Processing (2017, background): “A publicly available polarity classifier which produces such an output is SentiStrength (Thelwall et al., 2010).”
Understanding Human Mobility from Twitter (2014, methodology): “nts are used for tting), traditional methods like LSE are comparable to the state-of-the-art methods like MLE [1]. LSE combined with logarithmic binning can even perform better than MLE in some cases [41]. To demonstrate that P(d) with d2[100m;50km] corresponding to the regime of urban movements is ”
Lexicon-Based Methods for Sentiment Analysis (2011, result): “23 Our results are not comparable to those of Thelwall et al. (2010) on the MySpace comments, as they classify the comments on a 1–5 scale (obtaining average accuracy of 60.6% and 72.8% in positive and negative comments, respectively), whereas we have a much simpler two-point scale (positive or…”
Cyber-hate on social media in the aftermath of Woolwich (2015, methodology): “Several Content Factors were incorporated, including the sentiment expressed (using the established Sentistrength tool, Thelwall et al. 2010), the expression of hateful terms (see cyberhate models below), and the presence of hashtags and URLs (both of which improve discoverability and information…”
Contextual semantics for sentiment analysis of Twitter (2016, methodology): “We selected three state-of-art lexicons for this study: (i) the SentiWordNet lexicon (Baccianella et al., 2010), (ii) the MPQA subjectivity lexicon (Wilson et al., 2005) and, (iii) Thelwall-Lexicon (Thelwall et al., 2010, 2012).”
Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making (2015, methodology): “We removed all tweets with less than 75 percent agreement and also those upon which the coders could reach an absolute decision (i.e., the “undecided” class)—again, following established methods from related research (Thelwall et al., 2010a).”
Aggression Identification Using Deep Learning and Data Augmentation (2018, methodology): “Polarity classifiers for short texts, such as SentiStrength (Thelwall et al., 2010), are suited to extract such words of polarity and we make use of a polarity classifier in our approach.”