A Statistical Parsing Framework for Sentiment Classification
Table 6: Comparing our dictionary rule learning method with naive counting. The dictionary rules which are assigned different polarities by these two methods are presented. N represents negative, and P represents positive. The polarity probabilities of fragments are shown in this table, and they demonstrate our method learns more intuitive results than counting directly.
['Fragment', 'Naive Count N', 'Naive Count P', 'Naive Count Polarity', 's.parser N', 's.parser P', 's.parser Polarity']
We further investigate the effect of context for dictionary rule learning. We notice that if we count the fragment occurrence number directly, some polarities of fragments are learned incorrectly. By taking the context into consideration, we obtain more reasonable polarity probabilities of dictionary rules. Our dictionary rule learning method takes compositionality into consideration, i.e. we skip the count if there exist some negation indicators outside the phrase. This constraint tries to ensure that the polarity of fragment is the same as the whole sentence. As shown in the results, the polarity probabilities learned by our method are more reasonable and meet people’s intuitions. However, there are also some negative examples caused by “false subjective”. For instance, the neutral phrase “to pay it” tends to appear in negative sentences, and it is learned as a negative phrase. This makes sense for the data distribution, while it may lead to the mismatch for the combination rules.