Sentimental Analysis on Sarcasm Detection with GPS Tracking

Abstract

Sentimental analysis which is also known as opinion mining is one of the major tasks of natural language processing (NLP). Sentimental analysis is a technique which is used to identify a person’s sentiment, humor and their emotion. Sarcastic comments imply what a person wants to say in a conflicting manner. Sarcasm is being generally used among numerous informal communication and smaller scale blogging sites where individuals attack others which makes tricky for the person to state what it implies. For example, many sarcastic tweets which gives a positive impact like, “Technical talk right after lunch” but it describes an undesirable activity. There are number of researches done in sarcasm. In this paper feature extraction techniques were used, for instance, logistic regression, support vector machine (SVM), random forest, etc. to recognize sarcasm in tweets from the twitter gliding API. The perfect classifier is picked then joined along different pre-handling, separating methods utilizing sarcastic and non-sarcastic lexicon mapping to give the most ideal precision. A GPS tracking system is used collect the data and allocate to which location the Tweets are coming from. The sarcastic and non-sarcastic word reference being the shrewd idea introduced in this paper.