Product Sentimental Polarity in Python

Polarity shifting has been a challenge to automatic sentiment classification. In this paper, we create a corpus which consists of polarity-shifted sentences in various kinds of product reviews. In the corpus, both the sentimental words and shifting trigger words are annotated. Furthermore, we analyze all the polarity shifted sentences and categorize them into five categories: opinion-itself, holder, target, time and hypothesis. Experimental study shows the agreement of annotation and the distribution of the five categories of polarity shifting.