A Classification of Adjectives for Polarity Lexicons Enhancement
This addresses a key bottleneck in sentiment analysis by improving polarity lexicons for more accurate opinion extraction, though it is incremental as it builds on existing lexicon methods.
The paper tackled the problem of adjectives changing their prior polarity across domains in sentiment analysis, proving that a majority of adjectives are domain-dependent and proposing a classification to enhance polarity lexicons.
Subjective language detection is one of the most important challenges in Sentiment Analysis. Because of the weight and frequency in opinionated texts, adjectives are considered a key piece in the opinion extraction process. These subjective units are more and more frequently collected in polarity lexicons in which they appear annotated with their prior polarity. However, at the moment, any polarity lexicon takes into account prior polarity variations across domains. This paper proves that a majority of adjectives change their prior polarity value depending on the domain. We propose a distinction between domain dependent and domain independent adjectives. Moreover, our analysis led us to propose a further classification related to subjectivity degree: constant, mixed and highly subjective adjectives. Following this classification, polarity values will be a better support for Sentiment Analysis.