In sentiment analysis, polarity shifting means shifting the polarity of a sentiment clue
that expresses emotion, evaluation, etc. Compared with other natural language
processing (NLP) tasks, extracting polarity shifting patterns from corpora is a
challenging one because the methods used to shift polarity are flexible, which
often invalidates fully automatic approaches. In this study, which aimed to
extract polarity shifting patterns that inverted, attenuated, or canceled polarity,
we used a semi-automatic approach based on sequence mining. This approach
greatly reduced the cost of human annotating, while covering as many frequent
polarity shifting patterns as possible. We tested this approach on different domain
corpora and in different settings. Three types of experiments were performed and the
experimental results were analyzed, which will be reported in this paper.

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• R E S EAR CH Open Access Extracting Chinese polarity shifting patterns from massive text corpora