Tracking the effect of change a service on the telecom customer feeling is very important analysis for Telecom Companies. As a result of fast growth and severe competition, customer retention and managing high churn rate are the most important challenges faced by telecom companies today. Customer retention can be achieved by identifying the feeling of the telecom customers after changing a service and take care of telecom customers by modifying the services that reach a low score of customer willing. This paper was done by using a combination of four stages of text preprocessing, personality analysis, and sentiment analysis and chatbot system is created to achieve the needed task. This paper shows the effect of using the personality traits (agreeableness, emotional range) with sentiment analysis that help for reaching to a full description about customer feel. The proposed solution achieved the accuracy of 95% in determining the customer feel. Combining the Sentiment Analysis ‘Naïve Bayes technique’ in the natural language processing and personality insights pre-learning stage and adding a feedback using the obtained results achieve higher accuracy than using the traditional sentiment analysis techniques.