Due to the rapidly growing of E-commerce, more online reviews for products and services are created. Text Mining is used to extract valuable information from large amount of data. A key component is utilized to interface together the extricated data to frame new realities or new theories to be investigated further by more customary method for experimentation. There are several challenges in Sentiment analysis. The first is that an opinion word that is considered to be positive in one situation may be considered negative in another situation. The second challenge is that people don’t always express opinions in the same way. The usual text processing relies on the fact that small differences between two pieces of text don’t change the meaning very much. Sentiment analysis helps to find words that indicate sentiment and helps to understand the relationship between textual reviews and the consequences of those reviews. The proposed system shows that a simplified version of the sentiment-aware autoregressive model can produce very good accuracy for predicting the box office sale using online review data. It uses document level sentiment analysis that consists of Term frequency and Inverse Document frequency. In this process, mining techniques are applied on online Products reviews and predict the collection of the product based on the reviews and analyses how much effect the reviews have on the box office collection. Products collection for the next day is predicted based on online reviews of the present day. A prediction of high or low collection is also predicted.