Design of a personalized recommender system using sentiment analysis in social media (case study: banking system).

Customer retention and finding a way to preserve the customers are the most important issues of any organization. The main purpose of the present study in machine learning is to focus on correctly identifying customer needs with a method based on extracting opinion and sentiment analysis and quantifying customers' sentiment orientation. In other words, the main issue is designing a recommender system to provide appropriate services according to customer satisfaction, sentiment, and experiences. The proposed method is based on customers' opinions and experiences, which are obtained by evaluating tweets containing hashtags with the titles and headings of banking services as a statistical population. So, after reconsideration, correlation scores in terms of people's sentiment score due to the tweets, cosine similarity, and reliability, consideration of relevant characteristic groups as well as recorded ideas in the training and testing process will be provided in the form of submitting a personalized offer to receive banking services. In order to represent a recommending solution, suitable classification methods are used along with the opinion mining methods and proper validation approach as well, and the terminal designed system with a little error will take steps to provide personalized services as well as help the banking system. As there is no thorough provision of banking services tailored to the customers' situation, therefore, the mentioned system will be extremely beneficial. [ABSTRACT FROM AUTHOR]