Abstract
Micro blogging sites and online platforms are prevalent mediums of choices these days to express views, thoughts, and opinions on various topics, events etc. throughout the year. The text/comments/opinions are entered using smart IoT based devices. Opinion mining can be applied to analyse such large amount of textual data. One interesting analysis is the ability to automatically detect sarcasm from the opinions entered by people online, use it for various applications domains and to indicate personality trait(s) of people, sarcastic or non-sarcastic. This research work aims to achieve more accuracy, for sarcasm detection, than the prevalent approaches by focusing on the data cleaning process. The purpose is to identify the levels of sarcasm from the text written by the users on social media blogs and online articles and determine their personality traits and any changes observed in the personality traits over a period of time. This classification is achieved using supervised classification algorithms and a comparative study is performed. Gender-based experiments are conducted to observe changes in the level of sarcasm and personality traits in both the genders along with bloggers from varied professions. The outcome of this research is to understand effect of events, seasons, gender, profession etc. on sarcasm and personality traits over the period of time.
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Mulay, P., Joshi, R.R., Misra, A., Raje, R.R. (2019). Detection of Personality Traits of Sarcastic People (PTSP): A Social-IoT Based Approach. In: Balas, V., Solanki, V., Kumar, R., Khari, M. (eds) Internet of Things and Big Data Analytics for Smart Generation. Intelligent Systems Reference Library, vol 154. Springer, Cham. https://doi.org/10.1007/978-3-030-04203-5_11
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