Plebiscite prediction using social sentiment analysis
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Date
2022
Authors
IQRA MUBARIK
Journal Title
Journal ISSN
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Publisher
UMT, Lahore
Abstract
The rise of social media, Now the modern customers now have a powerful voice. Businesses (or analogous organizations) need to know the polarity of these views in order to have a deeper understanding of user orientation and make smarter decisions. A good example of where this might be beneficial is in politics, where a candidate's or party's fortunes may rise or fall depending on how accurately they predict voter opinion. Some have discovered that analyzing social media data for sentiment is a helpful way to track user preferences and habits. Both Naive Bayes and Support Vector Machines (SVM) are supervised learning algorithms that are often used for text categorization, and both need a training data set in order to perform sentiment analysis. Whether or not these algorithms succeed depends heavily on the quantity and quality (features and contextual relevance) of the tagged training data. Because of a dearth of training data, most applications rely on cross-domain sentiment analysis, which fails to capture details unique to the target data. It reduces the accuracy of text classification as a whole. Here, we offer a two-stage approach that may be used to produce training data from the mined Twitter data without compromising features or contextual relevance. Next, we provide a machine-learning model for election prediction that is scalable and based on our two-stage procedure.