Abstract
One of the simplest methods to understand people's thoughts using images or text is commonly given as sentiment analysis. Sentiment analysis is used mostly in products advertisement and promotion depends on the user’s opinion. The process is based on the aspect-based sentiment analysis and it is used to understand and find out what someone is speaking about, and likeness and dislikeness. One of the real-world models of the perfect realm of this subject is the huge number of available Indian welfare plans like Swachh Bharat Abhiyan and Jan Dhan Yojna. In this paper, labeled data is used on the basis of polarity. Tweets are preprocessed and unigram features are then extracted. In the initial steps, tokenization process, stop word removal process, and stemming process are performed as preprocessing to remove duplicate data. The unigram features and labels trained by support vector machine (SVM), K-nearest neighbor (KNN), and a combination of SVM, KNN, and random forest as a proposed model are used in the presented work. Implementation of experimental proposed approach demonstrates that better results in accuracy and precision than SVM and KNN.
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Kaur, M., Girdhar, A., Singh, I. (2021). Efficacy of Indian Government Welfare Schemes Using Aspect-Based Sentimental Analysis. In: Pandian, A., Fernando, X., Islam, S.M.S. (eds) Computer Networks, Big Data and IoT. Lecture Notes on Data Engineering and Communications Technologies, vol 66. Springer, Singapore. https://doi.org/10.1007/978-981-16-0965-7_74
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