Abstract
Deriving intelligence from text is important as it can provide valuable information on how events influence public opinion. In this work, a classification task was done in order to obtain the sentiment behind the polarity of an economic text using machine learning and deep learning methods. We analyzed the text for keywords that can be categorized into positive, negative and neutral reviews and found more insights. In the final result of classifying three groups (positive, negative and neutral), the models were unable to perform up to 80% accuracy, where only one variant has the accuracy of 80% as the best on the test dataset.
This work has been possible thanks to the support of the Government of Mexico via CONACYT, SNI, grant A1-S-47854; and Instituto Politécnico Nacional (IPN), grants SIP 2083, SIP 20200811 and SIP 20200859, IPN-COFAA, IPN-EDI, and IPN-BEIFI.
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Ojo, O.E., Gelbukh, A., Calvo, H., Adebanji, O.O., Sidorov, G. (2020). Sentiment Detection in Economics Texts. In: Martínez-Villaseñor, L., Herrera-Alcántara, O., Ponce, H., Castro-Espinoza, F.A. (eds) Advances in Computational Intelligence. MICAI 2020. Lecture Notes in Computer Science(), vol 12469. Springer, Cham. https://doi.org/10.1007/978-3-030-60887-3_24
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