Classification of Airline Tweet Using Naïve-Bayes Classifier for Sentiment Analysis
Nand Kishore Sharma, Surendra Rahamatkar, Sachin Kumar Sharma · 2019
Wide range of customers like family persons, business man, sportsman and youth are traveling via Airline. Hence feedback of persons matters a lot if they are involved. Direct feedbacks of customer may be positive or negative but analyzing their Tweets is important for the betterment i.e. how the Tweets is? Analysis of individual tweet is so much critical if the volume is high. Most of the times Tweets are ambiguous, which depends on the nature of customers i.e. positive person will always give positive Tweets and in other side negative tweets come from negative person. So ultimately, our work is to find the sentiments of descriptive Tweets as a result via their words and their expression in quantities format whether they are happy or not. The novel factor inside this work is to examine ambiguous Tweets and neutralize them according to proposed algorithm. The complete work is based on twitter dataset, here we are using US airline and performs different level of mining and processing for getting most accurate results. Here, improved sentiment analysis model has been proposed based on naïve Bayes classifier to classify tweets based on sentiments and neutralized tweets from ambiguous to positive or negative.