In this paper, we focus on the problem of the subjective classification of tweet dataset provided by the Health Science Research Team at The University of Illinois at Chicago (UIC) posted in May/June 2012įirst, we start our approach by performing the data preprocessing of the tweets using the following steps including but not limited 1 A new electronic gadget release, a natural calamity, terrorist incidents and elections are a few to name. Its rapid growth makes it ideal for diverse research as its humongous data, in its entirety, reflects people’s reaction, views, emotions, feelings of disgust, and sentiments on variety of incidents. INTRODUCTION Twitter is a popular online “micro blogging” service that enables users to exchange very short messages of length 140 characters. We show the performances of Naïve Bayes and Support Vector Machines (SVM). Our aim is to analyze the performance of different supervised learning models with interest in the views to quit smoking using two classes namely “Cessation” – which express view to quit smoking, and “No Cessation” – which do not express view to quit smoking. Problem Summary The point of interest in this research is the mention about quitting smoking (Class: Cessation) in the text tweet, the username, or the URL. The tweet also contains metadata like tweet id, follower count, friend count and the posted time, which might be useful. The statistics of the corpus are as follows: Tweet Types Total Training Total Testing Total Cessation Total Non CessationĢ/3rd of the data will be used as training data for the classifiers and the remaining 1/3rd will be used as the test data. We also include the effects of each feature set and suggest ideas to improve the classification accuracy.Ĭontaining keywords about electronic cigarette keywords. We create different feature sets and analyze the classification performances of two classes namely “Cessation” and “No Cessation” using Naïve Bayesian and SVM classifiers. Our aim is to subjectively classify tweets based on their content and sentiment using different NLP Classifier Models and analyze their performance. Here, we use one such dataset from Twitter which contains tweets about 'Quitting smoking or Not'. There are a lot of tweets about various health problems, their preventive measures and much more. Analysis of Supervised Learning for Subjective Classification of ‘Quit smoking or not’ tweets Mohanakrishnakumar Karunakaranĭepartment of Computer Science University of Illinois at Chicago Ībstract - Social media platforms such as Twitter, Facebook are rapidly becoming key resources for public health surveillance, which aid in the introduction and review of new products in the market.

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