Abstract
Sentiment Analysis has paved routes for opinion analysis of masses over unrestricted territorial limits. With the advent and growth of social media like Twitter, Facebook, WhatsApp, Snapchat in today’s world, stakeholders and the public often takes to expressing their opinion on them and drawing conclusions. While these social media data are extremely informative and well connected, the major challenge lies in incorporating efficient Text Classification strategies which not only overcomes the unstructured and humongous nature of data but also generates correct polarity of opinions (i.e. positive, negative, and neutral). This paper is a thorough effort to provide a brief study about various approaches to SA including Machine Learning, Lexicon Based, and Automatic Approaches. The paper also highlights the comparison of positive, negative, and neutral tweets of the Sputnik V, Moderna, and Covaxin vaccines used for preventive and emergency use of COVID-19 disease.
Highlights
Today the world is a Machine Dependency era
Today social media like Twitter serves as major platforms where users freely expresses their opinions and it is accessible within remote areas
Moderna is most positively and negatively discussed vaccine among twitter. These results are based upon observations made on February 2021. they may vary for another time values [36,37,38,39,40,41]
Summary
Today the world is a Machine Dependency era. Well-formed systems for information exchange from peer to peer or B2B are established. Today social media like Twitter serves as major platforms where users freely expresses their opinions and it is accessible within remote areas. Dwelling into study of Sentiment Analysis involves descriptive study of SA via Machine Learning and Lexicon based approaches. Emotion based aims at detecting emotions, like happiness, frustration, anger, sadness, and so on while Aspect Based analyze sentiments of texts, let’s say product reviews to know which particular aspects has positive, neutral, or negative way. The paper is arranged as follows: Section 2 is the related work of the studies conducted by researchers using Twitter Data.
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