Feature analysis Clause Samples

Feature analysis. To analyse the usage of each feature in the model including the TextBlob features described in the previous section, a Scikit-learn attribute that shows the coefficients of the features in the trained model has been used to make figure 4. This ranks every feature used by the model based on how much it is used to determine agreement or disagreement from most to least. An interesting observation is that the positive-negative relative polarity feature scores very well at 1.68, while the negative-positive relative polarity has one of the lowest scores. Negative-negative polarity patterns also score low as a coefficient of the model, which points towards that entries that contain negative proposals are harder to predict on agreement or disagreement regardless of the response. A response can agree with the negative proposal and in turn result in a positive response, or a response can repeat what the proposal said and be negative. To illustrate this thought, here are two examples: 1. P) ”That is a horrible idea.” R) ”I agree.” 2. P) ”That is a horrible idea.”