Abstract
In today’s competitive and result-driven environment, every decision needs to be carefully weighed before implementation. The government faces a similar problem while evaluating its schemes and forming policies. The goal of this study is to recommend a suitable and optimal automated tool for a highly accurate process of sentiment analysis on government schemes. This paper studies the effects of adding a feature selection phase to the conventional opinion mining model by analyzing the impact on the accuracy of the different models. For this purpose, swarm evolutionary algorithms, namely binary cuckoo search algorithm and firefly algorithm, have been used and analyzed, coupled with TF-IDF-based feature extraction for an optimized opinion classification process. Digital India, the flagship campaign of the Indian government, has been selected as the topic of study for this research due to its significant impact on Indian society in recent years. This paper aims to examine the success of the Digital India program, while at the same time, determine the most appropriate model for future assessment of government schemes.
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Sharma, A., Arora, N., Sachdeva, P. (2021). Enhanced Opinion Classification Using Nature-Inspired Meta-Heuristics for Policy Evaluation. In: Ranganathan, G., Chen, J., Rocha, Á. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 145. Springer, Singapore. https://doi.org/10.1007/978-981-15-7345-3_29
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