Smart Analysis of Economics Sentiment in Spanish Based on Linguistic Features and Transformers
Texts related to economics and finances are characterized by the use of words and expressions whose meaning (and the sentiments they convey) substantially depend on the context. This poses a major challenge to Natural Language Processing tasks in general, and Sentiment Analysis in particular. For lo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10041929/ |
| _version_ | 1797905471210782720 |
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| author | Jose Antonio Garcia-Diaz Francisco Garcia-Sanchez Rafael Valencia-Garcia |
| author_facet | Jose Antonio Garcia-Diaz Francisco Garcia-Sanchez Rafael Valencia-Garcia |
| author_sort | Jose Antonio Garcia-Diaz |
| collection | DOAJ |
| description | Texts related to economics and finances are characterized by the use of words and expressions whose meaning (and the sentiments they convey) substantially depend on the context. This poses a major challenge to Natural Language Processing tasks in general, and Sentiment Analysis in particular. For low-resource languages such as Spanish, this situation becomes even more acute. Yet, the latest advancements in the field, including word embeddings and transformers, have allowed to boost the performance of Sentiment Analysis solutions. In this work we explore the impact of the combination of different feature sets in the accuracy of Sentiment Analysis in Spanish financial texts. For this, a corpus with 15,915 tweets has been compiled and manually annotated as either positive, negative, or neutral. Then, feature sets based on contextual and non-contextual embeddings along with linguistic features were evaluated both individually and combined. The best results, with a weighted F1-score of 73.15880%, were obtained with a combination of feature sets by means of knowledge integration. |
| first_indexed | 2024-04-10T10:05:51Z |
| format | Article |
| id | doaj.art-8e86091550b649759a4d7bf83a58c539 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T10:05:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8e86091550b649759a4d7bf83a58c5392023-02-16T00:00:19ZengIEEEIEEE Access2169-35362023-01-0111142111422410.1109/ACCESS.2023.324406510041929Smart Analysis of Economics Sentiment in Spanish Based on Linguistic Features and TransformersJose Antonio Garcia-Diaz0https://orcid.org/0000-0002-3651-2660Francisco Garcia-Sanchez1https://orcid.org/0000-0003-2667-5359Rafael Valencia-Garcia2https://orcid.org/0000-0003-2457-1791Facultad de Informática, Universidad de Murcia, Campus de Espinardo, Murcia, SpainFacultad de Informática, Universidad de Murcia, Campus de Espinardo, Murcia, SpainFacultad de Informática, Universidad de Murcia, Campus de Espinardo, Murcia, SpainTexts related to economics and finances are characterized by the use of words and expressions whose meaning (and the sentiments they convey) substantially depend on the context. This poses a major challenge to Natural Language Processing tasks in general, and Sentiment Analysis in particular. For low-resource languages such as Spanish, this situation becomes even more acute. Yet, the latest advancements in the field, including word embeddings and transformers, have allowed to boost the performance of Sentiment Analysis solutions. In this work we explore the impact of the combination of different feature sets in the accuracy of Sentiment Analysis in Spanish financial texts. For this, a corpus with 15,915 tweets has been compiled and manually annotated as either positive, negative, or neutral. Then, feature sets based on contextual and non-contextual embeddings along with linguistic features were evaluated both individually and combined. The best results, with a weighted F1-score of 73.15880%, were obtained with a combination of feature sets by means of knowledge integration.https://ieeexplore.ieee.org/document/10041929/Sentiment analysisfinancialtransformersfeature engineeringdeep learning |
| spellingShingle | Jose Antonio Garcia-Diaz Francisco Garcia-Sanchez Rafael Valencia-Garcia Smart Analysis of Economics Sentiment in Spanish Based on Linguistic Features and Transformers IEEE Access Sentiment analysis financial transformers feature engineering deep learning |
| title | Smart Analysis of Economics Sentiment in Spanish Based on Linguistic Features and Transformers |
| title_full | Smart Analysis of Economics Sentiment in Spanish Based on Linguistic Features and Transformers |
| title_fullStr | Smart Analysis of Economics Sentiment in Spanish Based on Linguistic Features and Transformers |
| title_full_unstemmed | Smart Analysis of Economics Sentiment in Spanish Based on Linguistic Features and Transformers |
| title_short | Smart Analysis of Economics Sentiment in Spanish Based on Linguistic Features and Transformers |
| title_sort | smart analysis of economics sentiment in spanish based on linguistic features and transformers |
| topic | Sentiment analysis financial transformers feature engineering deep learning |
| url | https://ieeexplore.ieee.org/document/10041929/ |
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