Sentiment Analysis From Subjectivity to (Im)Politeness Detection: Hate Speech From a Socio-Pragmatic Perspective

Although sentiment analysis by definition is that field of Natural Language processing which focuses on analyzing texts that tackle evaluating, analyzing and detecting the state of mind of the human beings towards a range of domains, most of the studies limit it to opinion mining. Yet, opinion mining is just one sub-field of three others under the umbrella of sentiment analysis which are; opinion mining, emotion mining and ambiguity detection. Noticeably, ambiguity detection is considered to be a combination of the other two sub-fields thanks to its linguistic nature that considers statistical and/or syntactic-semantic levels of analysis are not adequate to reach a satisfying level of disambiguating human language. Henceforth, the current paper proposes digging deeply to reach pragmatic and socio-pragmatic levels of analysis in order to eliminate ambiguity and avoid misjudgments over texts and social media posts specifically in the sub-tasks of detecting hate speech. Accordingly, it suggests utilizing an eclectic linguistic model of analysis includes speech act theory and the theory of (im)politeness.