Sentiment in short strength detection informal text

By ThelwallMike, BuckleyKevan, PaltoglouGeorgios, CaiDi, Arvid Kappas. J. Assoc. Inf. Sci. Technol., 2010

1,043 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5b2bc4aaa63412ff1745a79d2f322b5ff67d0f9c

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In Artificial Intelligence

Citations in the corpus

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.”
Evaluation Datasets for Twitter Sentiment Analysis: A survey and a new dataset, the STS-Gold (2013, background): “Instead of the final sentiment labels associated to the tweets, some datasets provide a numeric sentiment strength between -5 and 5 defining a range from negative to positive polarity [24,25].”
Approaches, Tools and Applications for Sentiment Analysis Implementation (2015, background): “uk/Download) that is considered by [40] “the most popular stand-alone sentiment analysis tool”.”
Like It or Not (2016, methodology): “There have been attempts to train algorithms that alleviate some of these limitations [Thelwall et al. 2010], but they necessitate frequent retraining.”
Quantifying Political Leaning from Tweets and Retweets (2013, methodology): “Extracting Tweet Sentiment We applied SentiStrength (Thelwall et al. 2010), a lexicon-based sentiment analysis package, to extract the sentiment of tweets.”
Predicting Pre-click Quality for Native Advertisements (2016, methodology): “Aggregated Polarity [34] 2 Sum of Sentistrength positive and negative polarity for the overall”
SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis (2020, methodology): “We used these six datasets to compare SenticNet 6 with 15 popular sentiment lexica, namely: ANEW [5], a list of 1,030 words created in 1999; WordNet-Affect [50], an extension of WordNet made of 4,787 words developed in 2004; Opinion Lexicon [22], a lexicon of 6,789 words built in the same year by me”
A survey of multimodal sentiment analysis (2017, methodology): “Improving sentence-level unsupervised classification is the aim of a wide variety of unsupervised sentiment analysis methods (see [70] for a more in depth review on this subtopic), and it has led to three of the best performing methods in recent independent benchmarks [38]: SentiStrength [71], VADER”
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).”
This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News (2017, methodology): “SentiStrength is a sentiment analysis tool that reports a negative sentiment integer score between -1 and -5 and a positive sentiment integer score between 1 and 5, where -5 is the most negative and 5 is the most positive.”
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.”