Sentiment analysis in Twitter

A comparative study

Authors

DOI:

https://doi.org/10.51252/rcsi.v3i1.418

Keywords:

sentiment analysis, learning, classification, twitter

Abstract

Sentiment analysis helps to determine the perception of users in different aspects of daily life, such as product preferences in the market, level of user confidence in work environments, or political preferences. The idea is to predict trends or preferences based on feelings. In this article we evaluate the most common techniques used for this type of analysis, considering machine learning and deep machine learning techniques. Our main contribution is based on a proposal for a methodological strategy that covers the phases of data preprocessing, construction of predictive models and their evaluation. From the results, the best classical model was SVM, with 78% accuracy, and 79% F1 metric (F1 score). For the Deep Learning models, the classical models had the best results. The model with the best performance was the Deep Learning Long Short Term Memory (LSTM), reaching 88% accuracy and 89% F1 metric. The worst of the Deep Learning models was the CNN, with 77% accuracy as an F1 metric. Concluding that the Long Short Term Memory (LSTM) algorithm proved to be the best performance, reaching up to 89% accuracy.

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Published

2023-01-20

How to Cite

Lovera, F. A., & Cardinale, Y. (2023). Sentiment analysis in Twitter: A comparative study. Revista Científica De Sistemas E Informática, 3(1), e418. https://doi.org/10.51252/rcsi.v3i1.418