Machine Learning Techniques in Sentimental Analysis

Samrajyam Singu

Abstract


Sentiment analysis has several uses, including selling, recommendation, and financial analysis. It is regarded as a procedure for determining the polarity of provided data. In the presence of millions of reviews, manually extracting sentiments, useful information, and semantics from opinion sources becomes tough. This study provides a comprehensive review of several machine-learning approaches and compares them based on their accuracy, advantages, and limits. The experimental results show that supervised machine learning techniques outperform unsupervised learning techniques in terms of accuracy.

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