Let's assume for the moment that we've already learned a real-valued weight for each of these features, and that the 6 weights corresponding to the 6 features are [2.5, −5.0, −1.2, 0.5, 2.0, 0.7], while b = 0.1. (We'll discuss in the next section how the weights are learned.) The weight w 1 , for example indicates how important a feature the number of positive lexicon words (great, nice, enjoyable, etc.) is to a positive sentiment decision, while w 2 tells us the importance of negative lexicon words. Note that w 1 = 2.5 is positive, while w 2 = −5.0, meaning that negative words are negatively associated with a positive sentiment decision, and are about twice as important as positive words.