Version 1
: Received: 4 March 2024 / Approved: 4 March 2024 / Online: 5 March 2024 (05:10:29 CET)
How to cite:
Corona López, C.; Urias Piña, J.; Lahoz-Beltra, R. A Method to Classify Texts Based on Sentiment Analysis and Machine Learning. Preprints2024, 2024030147. https://doi.org/10.20944/preprints202403.0147.v1
Corona López, C.; Urias Piña, J.; Lahoz-Beltra, R. A Method to Classify Texts Based on Sentiment Analysis and Machine Learning. Preprints 2024, 2024030147. https://doi.org/10.20944/preprints202403.0147.v1
Corona López, C.; Urias Piña, J.; Lahoz-Beltra, R. A Method to Classify Texts Based on Sentiment Analysis and Machine Learning. Preprints2024, 2024030147. https://doi.org/10.20944/preprints202403.0147.v1
APA Style
Corona López, C., Urias Piña, J., & Lahoz-Beltra, R. (2024). A Method to Classify Texts Based on Sentiment Analysis and Machine Learning. Preprints. https://doi.org/10.20944/preprints202403.0147.v1
Chicago/Turabian Style
Corona López, C., Jesus Urias Piña and Rafael Lahoz-Beltra. 2024 "A Method to Classify Texts Based on Sentiment Analysis and Machine Learning" Preprints. https://doi.org/10.20944/preprints202403.0147.v1
Abstract
In this paper we describe a method which combines sentiment analysis with machine learning techniques and/or multivariate statistical analysis. By applying this methodology it is possible to classify a collection of texts into two or more groups or clusters. On the basis of a number of previously defined clusters, the novelty of the outlined approach is the use of the sentiment analysis results as input to the machine learning model or multivariate statistical analysis. Once the classifier has been obtained, we can assign a given text into one of the pre-established clusters. The groups or clusters can represent different time periods, classes of texts transcribed from different conversations, etc. The method is illustrated through an example taken from one of the two studies in which we have applied this methodology. In one of the studies, the method was used to classify press news of a volcanic eruption, while in the other study it was used to classify the conversations recorded between a chatbot with different kinds of speakers (humans or chatbots). This last study was the seminal work in which we introduced this methodology.
Keywords
Sentiment analysis; text classification; machine learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
