Abstract

Sentiment Analysis is one of the key research areas under the machine learning. In this research, the sentiment analysis is applied on the tweets which are based on airlines services. Sentiment analysis is done to classify the sentiments into either positive or negative. Various supervised and unsupervised machine learning algorithms are applied and their accuracy scores are estimated. Based on the accuracy score estimation the best machine learning algorithm for sentiment analysis is identified. The Experiment is carried out with the help of 14640 Airlines related tweets. Support Vector Machine algorithm shows the highest performance accuracy results of 90% and the lowest accuracy result of 79% is given by Decision tree machine learning algorithm. The result shows Support Vector machine algorithm performs better for the sentiment analysis of airlines tweets.

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