A Social Media Text Analysis Technique Using ML Algorithm

Abstract

Data mining and ML approaches are used for analyzing the raw data. The analysis consequences has depends on the applications requirements. The use of ML has also been become popular in analyzing the social media data based on NLP for uncovering the sentiments on the social media text post. Social media has two sides brighten and dark. There are a number of creative ways to use the social media, but there are also some users that are accomplishing their toxic intensions using social media. The use of machine learning algorithms with the social media helps us to performing prediction, classification, categorization and finding relationship among social media data attributes. Therefore, in this paper we have exploring the difference among the social media data text mining technique based data analysis and also by using NLP based text classification. In this context a publically available social media dataset from Kaggle has been collected and a data model has been presented to classify social media text using support vector machine (SVM) and backpropagation neural network (BPN) classifiers. In order to extract the features we have used the TF-IDF and in second scenario we have used the Part of speech (POS) tagger. The obtained results demonstrate the performance of BPN based classifier is higher in both the scenarios of feature classification. Additionally in simple subject classification the TF-IDF based features providing more yield as compared to POS based features. Keywords—machine learning algorithm, supervised learning, social media data, NLP based features, classification performance.