Supervised Sentiment Analysis Algorithms

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Marianela Denegri Coria; Juan Carlos Morales Arevalo; Jorge Luis Hilario-Rivas; Jorge Rubén Hilario-Cárdenas and Justina Isabel Prado-Juscamaita

Abstract

Sentiment analysis is used to analyse customer sentiment by the process of using natural
language processing, text analysis, and statistics. A good customer survey understands the
sentiment of their customers—what, how and why they’re saying it. Sentiment dataset can be
found mainly in tweets, comments and reviews. Sentiment Analysis understands emotions
with the help of software, and it is playing an inevitable role in today’s workplaces.
Sentiment analysis for opinion mining has become an emerging area where more research
and innovations are done. Sentiment or opinion analysis based on a domain is done using
several algorithms. Machine learning is a concept among this area. In this, the main focus is
on the supervised sentiment analysis or opinion mining algorithms. Supervised learning is a
division coming under machine learning. Different methods of supervised learning and
sentiment analysis algorithms are considered and their mode of functioning is studied. Main
focus of this paper is on the recent trends of research and studies for sentiment classification,
taking into consideration the accuracy of different algorithmic techniques that can be
implemented for accurate prediction in sentiment Analysis.

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