In this paper, we discuss the problem of Sentiment Analysis in Customer Relationship Management (CRM) systems from Computer Science point of view. The role of Sentiment Analysis in business decision making process is an important role, especially in these days where people relay on online shopping and write their opinions describing the online purchased products. We have implemented two different approaches to deal with Sentiment Analysis. One approach is based on Natural Language Processing (NLP) algorithms, and the second approach is based on Machine Learning Probabilistic Classification. The NLP approach is based on manually extracting the opinion words and creating an algorithm to classify customer reviews based on the extracted features. It also includes extracting the aspects of the product and their semantic orientation percentages scores. The features extraction is based on the "Dependency Parsing" technique. The Machine Learning algorithm is a supervised learning algorithm that will use a labelled dataset to be trained, the data will be transformed using Term Frequency-Inverse Document Frequency text representation model. For experimental results, we have used a dataset of online customers reviews on a product, to simulate a CRM system. The Machine Learning model showed a better overall results than the NLP-based approach. But through the NLP-based approach we were able to extract the list of product's aspects.