OpinAIS: An Artificial Immune System-based Framework for Opinion Mining
Autor:
Baldominos, Alejandro
; Luis Mingueza, Nerea
; García del Pozo, Mª Cristina
Fecha:
2015Palabra clave:
Revista / editorial:
International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)Tipo de Ítem:
articleDirección web:
https://www.ijimai.org/journal/bibcite/reference/2487Resumen:
This paper proposes the design of an evolutionary
algorithm for building classifiers specifically aimed towards
performing classification and sentiment analysis over texts.
Moreover, it has properties taken from Artificial Immune
Systems, as it tries to resemble biological systems since they are
able to discriminate harmful from innocuous bodies (in this case,
the analogy could be established with negative and positive texts
respectively). A framework, namely OpinAIS, is developed
around the evolutionary algorithm, which makes it possible to
distribute it as an open-source tool, which enables the scientific
community both to extend it and improve it. The framework is
evaluated with two different public datasets, the first involving
voting records for the US Congress and the second consisting in a
Twitter corpus with tweets about different technology brands,
which can be polarized either towards positive or negative
feelings; comparing the results with alternative machine learning
techniques and concluding with encouraging results.
Additionally, as the framework is publicly available for
download, researchers can replicate the experiments from this
paper or propose new ones.
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