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Government-sponsored policy-making and scheme generations is one of the meansof protecting and promoting the social, economic, and personal development ofthe citizens. The evaluation of effectiveness of these schemes done bygovernment only provide the statistical information in terms of facts andfigures which do not include the in-depth knowledge of public perceptions,experiences and views on the topic. In this research work, we propose animproved text classification framework that classifies the Twitter data ofdifferent health-based government schemes. The proposed framework leverages thelanguage representation models (LR models) BERT, ELMO, and USE. However, theseLR models have less real-time applicability due to the scarcity of the ampleannotated data. To handle this, we propose a novel GloVe word embeddings andclass-specific sentiments based text augmentation approach (named Mod-EDA)which boosts the performance of text classification task by increasing the sizeof labeled data. Furthermore, the trained model is leveraged to identify thelevel of engagement of citizens towards these policies in different communitiessuch as middle-income and low-income groups.

An Enhanced Text Classification to Explore Health based Indian Government Policy Tweets