ConnotationWordNet: Learning Connotation over the Word+Sense Network

Kang, Jahyo; Feng, Shuang; Akoglu, Leman; Choi, Yejin

doi:10.3115/v1/p14-1145

Cited by 24 publications

(34 citation statements)

References 26 publications

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“…A common way to harness a general-purpose lexicon for induction tasks in sentiment analysis is by using its glosses (Choi and Wiebe, 2014;Kang et al, 2014). Assuming that the explanatory texts of glosses are similar among abusive words, we treat glosses as a bag-of-words feature.…”

Section: Wordnet (Wn) and Wiktionary (Wk)mentioning

confidence: 99%

Inducing a Lexicon of Abusive Words – a Feature-Based Approach

Wiegand¹,

Ruppenhofer²,

Schmidt³

et al. 2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

109

142

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We address the detection of abusive words. The task is to identify such words among a set of negative polar expressions. We propose novel features employing information from both corpora and lexical resources. These features are calibrated on a small manually annotated base lexicon which we use to produce a large lexicon. We show that the word-level information we learn cannot be equally derived from a large dataset of annotated microposts. We demonstrate the effectiveness of our (domain-independent) lexicon in the crossdomain detection of abusive microposts.

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Section: Wordnet (Wn) and Wiktionary (Wk)mentioning

confidence: 99%

Inducing a Lexicon of Abusive Words – a Feature-Based Approach

Wiegand¹,

Ruppenhofer²,

Schmidt³

et al. 2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

109

142

View full text Add to dashboard Cite

show abstract

“…WordNet (Miller et al 1990) is the largest available ontology for English and a popular resource for sentiment analysis. Glosses, brief sense definitions, are a common feature for lexicon induction tasks in sentiment analysis (Esuli and Sebastiani 2005;Kang et al 2014). We expect that the glosses of shifters will share similar word choices.…”

Section: Wordnet (Wn)mentioning

confidence: 99%

“…Glosses , brief sense definitions, are a common feature for lexicon induction tasks in sentiment analysis (Esuli and Sebastiani 2005; Choi and Wiebe 2014; Kang et al . 2014). We expect that the glosses of shifters will share similar word choices.…”

Section: Feature Designmentioning

confidence: 99%

Automatic generation of lexica for sentiment polarity shifters

2020

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Abstract Alleviating pain is good and abandoning hope is bad. We instinctively understand how words like alleviate and abandon affect the polarity of a phrase, inverting or weakening it. When these words are content words, such as verbs, nouns, and adjectives, we refer to them as polarity shifters. Shifters are a frequent occurrence in human language and an important part of successfully modeling negation in sentiment analysis; yet research on negation modeling has focused almost exclusively on a small handful of closed-class negation words, such as not, no, and without. A major reason for this is that shifters are far more lexically diverse than negation words, but no resources exist to help identify them. We seek to remedy this lack of shifter resources by introducing a large lexicon of polarity shifters that covers English verbs, nouns, and adjectives. Creating the lexicon entirely by hand would be prohibitively expensive. Instead, we develop a bootstrapping approach that combines automatic classification with human verification to ensure the high quality of our lexicon while reducing annotation costs by over 70%. Our approach leverages a number of linguistic insights; while some features are based on textual patterns, others use semantic resources or syntactic relatedness. The created lexicon is evaluated both on a polarity shifter gold standard and on a polarity classification task.

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“…For instance, the arguments of "enjoy" are typically positive, while those of "suffer" are typically negative. Follow-up work showed that connotation can be associated with fine-grained word senses (Kang et al, 2014), but we limit our analysis of connotation at the word level at this stage.…”

Section: Marking Connotative Languagementioning

confidence: 99%

Connotation in Translation

Carpuat

2015

Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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We present a pilot study analyzing the connotative language found in a bilingual corpus of French and English headlines. We find that (1) manual annotation of connotation at the word-level is more reliable than using segment-level judgments, (2) connotation polarity is often, but not always, preserved in reference translations produced by humans, (3) machine translated text does not preserve the connotative language identified by an English connotation lexicon. These lessons will helps us build new resources to learn better models of connotation and translation.

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ConnotationWordNet: Learning Connotation over the Word+Sense Network

Cited by 24 publications

References 26 publications

Inducing a Lexicon of Abusive Words – a Feature-Based Approach

Inducing a Lexicon of Abusive Words – a Feature-Based Approach

Automatic generation of lexica for sentiment polarity shifters

Connotation in Translation

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