The social media has immense and popularity among all the services today. Sentiment Analysis is new way of machine learning to extract opinion orientation (positive, negative, neutral) from a text segment written for any product, organization and all other entities. In this research, design a framework for sentiment analysis with opinion mining for the case of airlines service feedback. Most available datasets of hotel reviews are not labelled which presents a lot of works for researchers as far as text data pre-processing task is concerned. Twitter is a SNS that has a huge data with user posting, with this significant amount of data, it has the potential of research related to text mining and could be subjected to sentiment analysis. The airline industry is a very competitive market which has grown rapidly in the past 2 decades. Airline companies resort to traditional customer feedback forms which in turn are very tedious and time consuming. In this work, worked on a dataset comprising of tweets for 6 major Indian Airlines and performed a multi-class sentiment analysis. This approach starts off with pre-processing techniques used to clean the tweets and then representing these tweets as vectors using a deep learning concept to do a phrase-level analysis. The analysis was carried out using 7 different classification strategies: Decision Tree, Random Forest, SVM, K-Nearest Neighbors, Logistic Regression, Gaussian Naïve Bayes and AdaBoost. The outcome of the test set is the tweet sentiment (positive/negative/neutral).

Sentiment Analysis, Machine Learning, Classification techniques, Deep Learning, Distributed Memory Model, Twitter Analysis

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