@article{gsitk2022araque,
author = "Araque, Oscar and S{\'a}nchez-Rada, J. Fernando and Iglesias, Carlos A.",
abstract = "GSITK is a framework to perform a wide variety of sentiment analysis tasks, including dataset acquisition, text preprocessing, model design, and performance evaluation. The framework is oriented to both researchers and practitioners, easing the replication of previous sentiment models, as well as offering implementations of common tasks. This is achieved by building several abstractions on top of popular libraries such as scikit-learn and NLTK. In this way, GSITK allows users to implement complex sentiment pipelines using comprehensible Python code. The framework is Open Source and has been used successfully in several research projects and competitions.",
comments = "JCR 2020 Q3 1.959,
SJR 2020 Q2 0.53,
Scopus 2020 Q2 2.8",
doi = "https://doi.org/10.1016/j.softx.2021.100921",
issn = "2352-7110",
journal = "SoftwareX",
keywords = "Sentiment analysis;word embedding;machine learning;natural language processing",
month = "1",
pages = "6",
title = "{GSITK}: {A} sentiment analysis framework for agile replication and development",
url = "https://www.sciencedirect.com/science/article/pii/S2352711021001643",
year = "2022",
}