Supervised Sentiment Analysis Algorithms

• Autores: Morales Arévalo, Juan Carlos, Denegri Coria, Marianela, Hilario Rivas, Jorge Luis, Hilario Cárdenas, Jorge Rubén, Prado Juscamaita, Justina Isabel
• Formato: artículo
• Fecha de Publicación: 2021
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/4571
• Enlace del recurso: https://hdl.handle.net/20.500.12867/4571
• Nivel de acceso: acceso abierto
• Materia: Opinion mining (análisis de sentimientos); Machine learning; Supervised learning; Aprendizaje supervisado; Aprendizaje automático; https://purl.org/pe-repo/ocde/ford#5.02.04

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