To better analyze the sentiment, attitude, emotions of users from written language, it is necessary to identify the sentiment polarity of each word not only the overall sentiment (positive/neutral/negative) of a given text. In this paper we propose a novel approach by using a method based on Neural Bag-Of-Words (NBOW) model combined with Ngram, aiming at achieving a good classification score on short text which contain less than 200 words along with sentiment polarity of each word. In order to verify the proposed methodology, we evaluated the classification accuracy and visualize the sentiment polarity of each word extracted from the model, the data set of our experiment only have the sentiment label for each sentence, and there is no information about the sentiment of each word. Experimental result shows that the proposed model can not only correctly classify the sentence polarity but also the sentiment of each word can be successfully captured.