Start-up companies use a variety of SaaS products to automate things and get the job done which is required by the consumers. SaaS products types of software that are hosted by a central provider and offered to customers through the internet. Rather than installing or downloading a copy of the application, users can access the product from a web or mobile browser. The SaaS company then manages and updates the software based on user needs. Often, they come across options for softwares which offers the same services yet have many differences. It takes alot of effort to decide which service suits the purpose well and is accustomed to the particular needs of the projects, thus it is a hard job to decide which product is best according to the company’s various needs and fits best accordingly. As it is a time consuming task for the companies to filter out the existing services we are going to minimize the time complexity of this task by bringing all the Saas Products to a single platform where the users can compare the services, products etc and use the services that serve their purpose optimally. In our work, we use twitter data to analyze public views towards a product. Firstly, we have developed a natural language processing (NLP) based pre[1]processed data framework to filter tweets. Secondly, we incorporate Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) model concept to analyze sentiment. This is an initiative to use BoW and TFIDF are used together to precisely classify positive and negative tweets. We have found that by exploiting TF-IDF vectorizer, the accuracy of sentiment analysis can be substantially improved and simulation results show the efficiency of our proposed system. We achieved 85.25% accuracy in sentiment analysis using NLP technique