On the Challenges of Detecting Rude Conversational Behaviour
Table 5: Classification results for identifying the beginning, middle, and end of a phrase. SVM classifiers with polynomial kernels perform remarkably worse and are omitted.
['SVM kernel', 'Accuracy (%) Regular', 'Accuracy (%) Smoothed,  [ITALIC] w=15']
We train a SVM classifier and K-means clustering algorithm (where K=3) to distinguish between the beginning, middle and end of each phrase. A SVM classifier with a Gaussian kernel and γ=0.5 achieves almost 80% accuracy on correctly identifying the end of the phrase, but is only able to identify the middle correctly about half the time. As mentioned in the previous section, the motivation for correctly identifying the end is to determine if a phrase is complete; if not, there is a chance that an interruption occurred. K-means also performs poorly, even when the number of instances which can be classified as a beginning, middle or end are restricted to satisfy proportions from the dataset. We restrict portions since the distribution between parts of a phrase is clearly not equal.