Machine Learning for Opinion and Sector Assessment of ‘Make in India’ Based on User Types

Abstract

Social Media now plays a significant role in shaping and impacting our attitude, behaviour and cultural aspects of our life. The general mood or opinion about a product or a subject matter can be derived by monitoring social media activities of individuals pertaining to the product or subject matter. Tracking these and analysing them helps in a more targeted advertising campaigns for example. The proposed work explores Sentiment Analysis study performed on a sample of Twitter posts on the subject of Make in India, a Govt. of India (GOI) initiative to hasten and support growth in the Indian manufacturing sector. This paper identifies the overall sentiment towards Make in India initiative and also identifies the sentiment towards various sectors which are part of the initiative. Additionally, the sentiment and sector classification are done separately based on the type of Twitter users, namely a personal user account or business/organizations. The latter type of user has a tendency to deeply influence and skew the sentiment results and therefore it is important to separate out this point of view during the analysis of public’s sentiment on the subject. The analysis is performed using machine learning approach. Unigram and Bigram feature extraction techniques are used and their correlation is calculated using Count Vectorizer and Term Frequency – Inverse Document Frequency (TF-IDF). The prediction is done using supervised machine learning algorithms and uses R Studio for the analysis.