Idiom-Based Features in Sentiment Analysis: Cutting the Gordian Knot

Spasić, Irena; Williams, Lowri; Buerki, Andreas

doi:10.1109/taffc.2017.2777842

Cited by 18 publications

(11 citation statements)

References 51 publications

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“…In some cases, virtually no variation is allowed without loss of the idiomatic sense, while other cases allow extensive types of variation. For the purpose of this study we focused on generalizing over two common types of variation (Spasic et al, 2017): (1) verb inflection (e.g., 'pick a fight' may surface as 'picking a fight' or 'picked a fight') and (2) open slots in expressions involving indefinite pronouns (e.g., 'swallow one's pride', 'mean the world to someone'), which generally are used with the indefinite pronoun substituted by a personal pronoun (e.g., 'swallow her pride', 'mean the world to me'). We refrain from handling additional variations -e.g., filling in open slots with noun phrases -due to the difficulty of accurate extraction and the lower frequency of these cases.…”

Section: Idiomatic Expressionsmentioning

confidence: 99%

Pick a Fight or Bite your Tongue: Investigation of Gender Differences in Idiomatic Language Usage

Rabinovich

Gonen

Stevenson

2020

Proceedings of the 28th International Conference on Computational Linguistics

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A large body of research on gender-linked language has established foundations regarding crossgender differences in lexical, emotional, and topical preferences, along with their sociological underpinnings. We compile a novel, large and diverse corpus of spontaneous linguistic productions annotated with speakers' gender, and perform a first large-scale empirical study of distinctions in the usage of figurative language between male and female authors. Our analyses suggest that (1) idiomatic choices reflect gender-specific lexical and semantic preferences in general language, (2) men's and women's idiomatic usages express higher emotion than their literal language, with detectable, albeit more subtle, differences between male and female authors along the dimension of dominance compared to similar distinctions in their literal utterances, and (3) contextual analysis of idiomatic expressions reveals considerable differences, reflecting subtle divergences in usage environments, shaped by cross-gender communication styles and semantic biases.

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Section: Idiomatic Expressionsmentioning

confidence: 99%

Pick a Fight or Bite your Tongue: Investigation of Gender Differences in Idiomatic Language Usage

Rabinovich

Gonen

Stevenson

2020

Proceedings of the 28th International Conference on Computational Linguistics

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“…The remainder of this section provides more detail on selection criteria. In previous studies, we demonstrated the value of idioms as pertinent features in sentiment analysis (Williams et al 2015;Spasić et al 2017). We found that idiom-based features significantly improve sentiment classification results.…”

Section: Text Corpusmentioning

confidence: 69%

Comparing the Utility of Different Classification Schemes for Emotive Language Analysis

et al. 2019

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In this paper we investigated the utility of different classification schemes for emotive language analysis with the aim of providing experimental justification for the choice of scheme for classifying emotions in free text. We compared six schemes: (1) Ekman's six basic emotions, (2) Plutchik's wheel of emotion, (3) Watson and Tellegen's Circumplex theory of affect, (4) the Emotion Annotation Representation Language (EARL), (5) WordNet-Affect, and (6) free text. To measure their utility, we investigated their ease of use by human annotators as well as the performance of supervised machine learning. We assembled a corpus of 500 emotionally charged text documents. The corpus was annotated manually using an online crowdsourcing platform with five independent annotators per document. Assuming that classification schemes with a better balance between completeness and complexity are easier to interpret and use, we expect such schemes to be associated with higher inter-annotator agreement. We used Krippendorff's alpha coefficient to measure inter-annotator agreement according to which the six classification schemes were ranked as follows: (1) six basic emotions (α = 0.483), (2) wheel of emotion (α = 0.410), (3) Circumplex (α = 0.312), EARL (α = 0.286), (5) free text (α = 0.205), and (6) WordNet-Affect (α = 0.202). However, correspondence analysis of annotations across the schemes highlighted that basic emotions are oversimplified representations of complex phenomena and as such likely to lead to invalid interpretations, which are not necessarily reflected by high inter-annotator

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“…These rules were idiom specific, i.e., only idioms that were included explicitly in the model could be matched in text. To address the issues of generalisability and scalability, we later developed an approach to derive the pattern-matching rules automatically from idioms' canonical forms, i.e., their corresponding entries in a monolingual dictionary [20]. Flor and Beigman Klebanov [21] implemented a similar rule-based generation of pattern-matching rules, which were evaluated on a corpus of essays written by non-native English speakers.…”

Section: Related Workmentioning

confidence: 99%

Leaving No Stone Unturned: Flexible Retrieval of Idiomatic Expressions from a Large Text Corpus

Hughes

Filimonov

Wray

et al. 2021

MAKE

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Idioms are multi-word expressions whose meaning cannot always be deduced from the literal meaning of constituent words. A key feature of idioms that is central to this paper is their peculiar mixture of fixedness and variability, which poses challenges for their retrieval from large corpora using traditional search approaches. These challenges hinder insights into idiom usage, affecting users who are conducting linguistic research as well as those involved in language education. To facilitate access to idiom examples taken from real-world contexts, we introduce an information retrieval system designed specifically for idioms. Given a search query that represents an idiom, typically in its canonical form, the system expands it automatically to account for the most common types of idiom variation including inflection, open slots, adjectival or adverbial modification and passivisation. As a by-product of query expansion, other types of idiom variation captured include derivation, compounding, negation, distribution across multiple clauses as well as other unforeseen types of variation. The system was implemented on top of Elasticsearch, an open-source, distributed, scalable, real-time search engine. Flexible retrieval of idioms is supported by a combination of linguistic pre-processing of the search queries, their translation into a set of query clauses written in a query language called Query DSL, and analysis, an indexing process that involves tokenisation and normalisation. Our system outperformed the phrase search in terms of recall and outperformed the keyword search in terms of precision. Out of the three, our approach was found to provide the best balance between precision and recall. By providing a fast and easy way of finding idioms in large corpora, our approach can facilitate further developments in fields such as linguistics, language education and natural language processing.

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Idiom-Based Features in Sentiment Analysis: Cutting the Gordian Knot

Cited by 18 publications

References 51 publications

Pick a Fight or Bite your Tongue: Investigation of Gender Differences in Idiomatic Language Usage

Pick a Fight or Bite your Tongue: Investigation of Gender Differences in Idiomatic Language Usage

Comparing the Utility of Different Classification Schemes for Emotive Language Analysis

Leaving No Stone Unturned: Flexible Retrieval of Idiomatic Expressions from a Large Text Corpus

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