The domain of intelligent question answering systems is leading the major role in fulfill the user requirement with specific answers stimulate QA research to the next level with machine learning techniques. In this paper, we present mobile based question answering system acts as a personal assistant in learning and for providing the user with information on computers, software and hardware, book reviews by using natural language for the communications. The proposed Mobile based QA models will accept the natural language query, analysis and match them with information stored in the knowledge base and display the optimized result. The knowledge base created from the benchmark data set such as Amazon book reviews, 20newsgroup and Yahoo! Answer data set clustered with content specific clustering and displays the outcome in the form of snippets as output. Sentiment analysis used to decrease the vocabulary gap among the user query and retrieved candidate answer solutions. The results of the proposed interface evaluated with standard metrics such as Precision, Recall, F1-Score, Inverse precision and Inverse recall for the appropriate return of relevant answer.