Systematic Review on Machine Learning Approaches for Sentiment Analysis

 

ABSTRACT

Classification of texts is a developed machine learning research field. 

Machine Learning is one of the most sought-after capabilities these days. 

This review paper provides an overview of recent changes to text classification algorithms and implementations. 

With the exponential development of social media, unstructured data is growing exponentially and the sophistication of machine learning is also increasing. 

Text mining strategies that aim to derive valuable information from textual data sources by finding fascinating trends are promising. 

Typically people write feedback on any category of products and services and place them on online forums. 

Potential consumers may be profoundly affected by the opinions of others on goods and services. 

Brand suppliers and marketing experts can keep track of consumer feedback about their products by

processing the ratings and can get higher user satisfaction. 

The suggested survey paper exploits the classification efficiency of two practical semantic method approaches for the assigning of online review classification based on emotion. 

While the approximate machine learning algorithms will decrease computation time, the accuracy of classification is drastically degraded.

Keywords- Sentiment Analysis, Text Mining, Machine Learning, Structured Data, Word Net.

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