| Challenge: | Existing sentiment lexicons are compiled by (machine) translation from English resources, obscuring language-specific characteristics of sentiment-loaded vocabulary. |
| Approach: | They propose a gold standard for sentiment annotation of Swedish terms using the SALDO lexicon and the Gigaword corpus. |
| Outcome: | The proposed model is based on the free SALDO lexicon and the Gigaword corpus and is compared with existing models using human annotations. |
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SenSALDO: Creating a Sentiment Lexicon for Swedish (L18-1)
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| Challenge: | sentiment analysis has seen an explosive expansion over the last decade or so . many theoretical and methodological questions remain unanswered and resource gaps unfilled . |
| Approach: | They develop a sentiment lexicon for written (standard) Swedish using an existing dataset . they assign a real value sentiment score in the range [-1,1] and produce a label for it . |
| Outcome: | The proposed sentiment lexicon is an open source resource from the Swedish Language Bank . it is based on an existing gold standard dataset and is available from Sprkbanken . |
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. |
| Outcome: | The proposed lexicon contains 13,859 Danish polarity lemmas and includes morphological information. |
Building Sentiment Lexicons for Mainland Scandinavian Languages Using Machine Translation and Sentence Embeddings (2022.lrec-1)
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| Challenge: | a simple but effective method to build sentiment lexicons for the three Mainland Scandinavian languages is proposed . a number of experiments with Scandinavian language datasets yield state-of-the-art results using a rule-based sentiment analysis algorithm. |
| Approach: | They propose a simple but effective method to build sentiment lexicons for the three Mainland Scandinavian languages. |
| Outcome: | The proposed method is based on the English Sentiwordnet and a thesaurus in one of the target languages. |
Odi et Amo. Creating, Evaluating and Extending Sentiment Lexicons for Latin. (2020.lrec-1)
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| Challenge: | a new paper aims to provide sentiment analysis tools for ancient languages . the current sentiment analysis resources only cover modern languages based on textual typologies . |
| Approach: | They propose to use manually-curated Latin lexicons to evaluate sentiment analysis tools . they propose a gold standard and a silver standard for evaluating lexical items . |
| Outcome: | The proposed lexicons are evaluated using a gold standard and a silver standard for sentiment analysis. |
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 . |
| Outcome: | The proposed method allows for the construction of a more reliable sentiment lexicon . the proposed method is partially automated, but it's performance drops in cross-domain applications . |
Representation Mapping: A Novel Approach to Generate High-Quality Multi-Lingual Emotion Lexicons (L18-1)
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| Challenge: | Existing representational frameworks for emotion encoding are incompatible with semantic polarity, resulting in a large amount of incompatible emotion lexicons. |
| Approach: | They propose to map different emotion representation formats onto each other for mutual compatibility and interoperability of language resources. |
| Outcome: | The proposed method produces (near-)gold quality emotion lexicons even in crosslingual settings. |
Domain-Specific Sentiment Lexicons Induced from Labeled Documents (2020.coling-main)
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| Challenge: | Existing sentiment lexicons reflect abstract notion of polarity and do not do justice to substantial differences of word polarities between domains. |
| Approach: | They propose to use domain-specific sentiment lexicons to induce initial word intensity scores and train new deep models based on word vector representations to overcome the scarcity of the seed data. |
| Outcome: | The proposed models show that they perform well on review classification and cross-lingual word sentiment prediction. |
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. |
| Outcome: | The proposed lexicon can be used to generate sentiment lexicos for English using the Open Multilingual Wordnet. |
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 . |
Learning and Evaluating Emotion Lexicons for 91 Languages (2020.acl-main)
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| Challenge: | Emotion lexicons describe the affective meaning of words but are limited in coverage for most languages. |
| Approach: | They propose a method for creating arbitrarily large emotion lexicons for any target language. |
| Outcome: | The proposed method exceeds human reliability for some languages and variables. |