The Development of Sentiment Analysis from a linguistic perspective

Abstract

Although sentiment analysis by definition is that field of Natural Language processing which focuses on analysing texts that tackle evaluating, analysing, 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 misjudgements over texts and social media posts specifically in the sub-tasks of detecting and disambiguating hate speech, irony and sarcasm. Finally, it suggests utilizing an eclectic linguistic model of analysis includes speech act theory and the theory of (im)politeness