Machine Learning Based Sentiment Analysis on Spanish Financial Tweets View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018

AUTHORS

José Antonio García-Díaz , María Pilar Salas-Zárate , María Luisa Hernández-Alcaraz , Rafael Valencia-García , Juan Miguel Gómez-Berbís

ABSTRACT

Nowadays, financial data on social networks play an important role to predict the stock market. However, the exponential growth of financial information on social networks such as Twitter has led to a need for new technologies that automatically collect and categorise large volumes of information in a fast and easy manner. The Natural Language Processing (NLP) and sentiment analysis areas can solve this problem. In this respect, we propose a supervised machine learning method to detect the polarity of financial tweets. The method employs a set of lexico-morphological and semantic features, which were extracted with UMTextStats tool. Furthermore, we have conducted a comparison of the performance of three classification algorithms (J48, BayesNet, and SMO). The results showed that SMO provides better results than BayesNet and J48 algorithms, obtaining an F-measure of 73.2%. More... »

PAGES

305-311

References to SciGraph publications

Book

TITLE

Trends and Advances in Information Systems and Technologies

ISBN

978-3-319-77702-3
978-3-319-77703-0

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-77703-0_31

DOI

http://dx.doi.org/10.1007/978-3-319-77703-0_31

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1101747663


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