Understanding public opinions on social media for financial sentiment analysis using AI-based techniques

Abstract

The digital currency has taken the financial markets by storm ever since its inception. Academia and industry are focussing on Artificial intelligence (AI) tools and techniques to study and gain an understanding of how businesses can draw insights from the large-scale data available online. As the market is driven by public opinions, and social media today provides an encouraging platform to share ideas and views; organizations and policy-makers could use the natural language processing (NLP) technology of AI to analyze public sentiments. Recently, a new and moderately unconventional instrument known as non-fungible tokens (NFTs) is emerging as an upcoming business market. Unlike the stock market, no precise quantitative parameters exist for the price determination of NFTs. Instead, NFT markets are driven more by public opinion, expectations, the perception of buyers, and the goodwill of creators. This study evaluates human emotions on the social media platforms Twitter posted by the public relating to NFTs. Additionally, this study conducts secondary market analysis to determine the reasons for the growing acceptance of NFTs through sentiment and emotion analysis. We segregate tweets using Pearson Product-Moment Correlation Coefficient (PPMCC) and study 8-scale emotions (Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, and Trust) along with Positive and Negative sentiments. Tweets majorly contained positive sentiment (∼ 72%), and positive emotions like anticipation and trust were found to be predominant all over the world. This is the first of its kind financial and emotional analysis of tweets pertaining to NFTs to the best of our understanding.