| Challenge: | Negation words can change the sentiment polarity of a phrase, but there are more than 1200 other polarities. |
| Approach: | They propose a lexicon of verbal polarity shifters that covers the entirety of verbs found in WordNet. |
| Outcome: | The proposed lexicon covers the entirety of verbs found in WordNet. |
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| Challenge: | Existing polarity shifter lexica only specify when a word can cause shifting, but do not specify when this is limited to a single shifting direction. |
| Approach: | They propose a classifier that determines the shifting direction of polarity shifters by using resource-driven features and data-driven feature. |
| Outcome: | The proposed classifier enhances the largest available polarity shifter lexicon. |
Automatically Creating a Lexicon of Verbal Polarity Shifters: Mono- and Cross-lingual Methods for German (C18-1)
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| Challenge: | a large number of verbal polarity shifters are available for multiple languages, but only English has a sizable lexicon of them. |
| Approach: | They use methods to create large lexicon of verbal polarity shifters in germany . they bootstrap annotated verbs with a supervised classifier and apply them to German . |
| Outcome: | The proposed method is able to create a large lexicon of verbal polarity shifters in germany . it reduces annotation effort by leveraging cross-lingual information from the English lexico . |
Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon (2021.findings-acl)
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| Challenge: | Existing studies on sentiment lexicons have focused on domain-dependent sentiment words. |
| Approach: | They propose a graph-based technique to detect and correct domain-dependent sentiment words . they propose to use a sentiment lexicon to classify sentiments in a lexical-based classifier . |
| Outcome: | The proposed method is effective on multiple datasets from different domains. |
Disambiguation of Verbal Shifters (L18-1)
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| Challenge: | Negation is a contextual phenomenon that needs to be addressed in sentiment analysis. |
| Approach: | They propose a supervised learning approach to disambiguate verbal shifters using generalization features and a new lexicon. |
| Outcome: | The proposed approach takes into account various features, particularly generalization features. |
Sense and Sentiment (2022.lrec-1)
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| Challenge: | Existing sentiment lexicons and concept-based sentiment-tagged corpora are not accurate, and it is difficult to map sentiment scores accurately to different languages. |
| Approach: | They examine existing sentiment lexicons and sense-based sentiment-tagged corpora to find out how sense and concept-based semantic relations effect sentiment scores. |
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Classifier-based Polarity Propagation in a WordNet (L18-1)
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| Challenge: | a wordnet-based sentiment lexicon can be built to express sentiment polarity in a way shared across domains. |
| Approach: | They propose a method to build a sense-level sentiment lexicon on the basis of a wordnet . they use a rich set of wordnet-based features to recognize and assign sentiment polarity values . |
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A Thesaurus-based Sentiment Lexicon for Danish: The Danish Sentiment Lexicon (2022.lrec-1)
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| Challenge: | a newly published Danish sentiment lexicon with a high lexical coverage was compiled using lexicographic methods and linked data. |
| Approach: | They propose to use lexicographic methods to compile a Danish sentiment lexicon with a high lexical coverage by linking words from a thesaurus to a comprehensive monolingual dictionary. |
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Transformers in the loop: Polarity in neural models of language (2022.acl-long)
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| Challenge: | Recent Transformer-based language representation models (LRMs) show impressive results on practical text analysis tasks, but do they have access to complex linguistic notions? |
| Approach: | They propose to use polarity as a case study to compare metrics derived from language models to human judgments obtained in psycholinguistic experiments. |
| Outcome: | The proposed model is more accurate than linguistic theory predictions for polarity, and allows us to use language models to discover new insights into natural language grammar beyond existing linguistic theories. |
Enhancing General Sentiment Lexicons for Domain-Specific Use (C18-1)
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| Challenge: | Lexicon based methods for sentiment analysis rely on high quality polarity lexicons. |
| Approach: | They evaluate SentProp framework for inducing domain-specific polarities from word embeddings and use it to enhance a general-purpose lexicon for use in the political domain. |
| Outcome: | The proposed framework performs worse than the original lexicon in an out-domain task, showing that the words added and the polarity shifts applied are domain-specific and do not translate well to an out domain setting. |
Lingmotif-lex: a Wide-coverage, State-of-the-art Lexicon for Sentiment Analysis (L18-1)
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| Challenge: | Sentiment Analysis is a subtask of Natural Language Processing. |
| Approach: | They propose a new, domain-neutral lexicon for sentiment analysis in English . they test it on two publicly available sentiment analysis datasets . |
| Outcome: | The proposed lexicon performs better than existing sentiment lexiconics on two publicly available datasets. |