A Comparative study on Airline Recommendation System Using Sentimental Analysis on Customer Tweets

Recommendation compels in many fields and has become a great immersion in the zone of research. Important factor for recommendation system is to recognize user’s personalized historic behaviors through analyzing different tweets made by them. Recommender systems have become common in the research, where many opinions are replenished on the grounds of algorithms. The paper is on comparative study of the techniques used for airlines recommendation systems. The basic motives behind this paper are 1)To build a model for airlines this performs sentiment analysis on customer reviews in order to have an attend concise feedback about the airlines. 2)Encouraging for the most important facet of advancement so they can improve the given customers’ complaints. Here, we perform Sentimental Analysis on the Twitter Airlines dataset from Kaggle. Vital accuracy is achieved, which shows the reliability of the project for future prediction.