Social Media User Evaluation for Quantum Computing Technology Via Sentiment Analysis

Abstract

Abstract Sentiment analysis is one of the most well-known applications of natural language processing (NLP) techniques used to determine a text's sentiment or emotional tone, such as a sentence, a paragraph, or an entire document. The goal of sentiment analysis is to identify and extract the underlying sentiment expressed by the author, whether positive or negative. Social media platforms like Twitter, Facebook, and Google + are quickly gaining popularity due to the ability for users to share and express their opinions on many subjects, engage in conversation with different organizations, and broadcast messages globally. Sentiment analysis has been extensively studied to track and understand developer comments and views. Quantum software engineering develops software for quantum computers, which use quantum computing to process data. It has gained significant prominence in the field of software technology. Quantum computing may tackle issues that classical computers cannot, advancing cryptography, optimization, and material science. This study aims to explore the social media user review for quantum computing technology innovation in the current era. For this purpose, sentiment analysis applies to social media user reviews for quantum computing technology use. The extracted data is scrubbed through preprocessing techniques. TextBlob, VADER, and supervised learning classification methods have analyzed the sentiments and topics extracted from social media. Results show that quantum users are satisfied with using this soft computing technology and find this experience a successful, positive review for innovative quantum computing technology.