Abstract
We provide an overview on the development of a fine-grained, aspect-based sentiment analysis approach aimed at providing useful signals to improve forecasts of economic models and produce more accurate predictions. The approach is unsupervised since it relies on external lexical resources to associate a polarity score to a given term or concept. After providing an overview of the method under development, some preliminary findings are also given.
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Notes
- 1.
Dow Jones DNA: Data, News and Analytics Platform: https://www.dowjones.com/dna/.
- 2.
World Bank Group Ontology, available at: http://vocabulary.worldbank.org/thesaurus.html.
- 3.
spaCy: Industrial-Strength Natural Language Processing. Available at: https://spacy.io/.
- 4.
- 5.
WordNet, A Lexical Database for English. Available at https://wordnet.princeton.edu/.
- 6.
SentiWordNet, available at http://swn.isti.cnr.it/.
- 7.
Loughran-McDonald Sentiment Word Lists, available at: https://sraf.nd.edu/textual-analysis/resources/.
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Barbaglia, L., Consoli, S., Manzan, S. (2020). Monitoring the Business Cycle with Fine-Grained, Aspect-Based Sentiment Extraction from News. In: Bitetta, V., Bordino, I., Ferretti, A., Gullo, F., Pascolutti, S., Ponti, G. (eds) Mining Data for Financial Applications. MIDAS 2019. Lecture Notes in Computer Science(), vol 11985. Springer, Cham. https://doi.org/10.1007/978-3-030-37720-5_8
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