Recommendation System with Sentiment Analysis as Feedback Component

In today’s world Artificial intelligence (AI) is known for deploying human like intelligence in to computers, so that they behave like humans. One of specialization areas of AI is expert systems. This area focuses on programming machines to take real life decisions. System with its intelligence helps users by suggesting them with variety of choices and making it easier for people to take best decisions while purchasing items. This work is intended to develop and deploy a Hotel Recommendation System. The work makes use of Collaborative user and item filtering techniques in combination with sentiment classification for generating recommendations. To improve the recommendations results, sentiment classification results are used as the feedback. There is also performance comparison between two different classifiers “Naïve Bayesian” (NB) and “K-Nearest Neighbor” (K-NN) with respect to their ability to recommend. This hybrid technique helps us in the case where an item has no ratings but has only textual reviews. Since this technique draws conclusion based on reviews along with the ratings, recommendation results are more accurate compared to recommendation systems based solely on filtering techniques.