Abstract
Sentiment classification for marketing block chain-based digital assets through Twitter data is a pivotal component of crypto currency marketing strategies. Twitter serves as a real-time crucible of opinions, discussions, and news within the crypto space, making it a treasure trove of sentiment insights. Employing advanced natural language processing and machine learning techniques, marketers can discern prevailing sentiment - whether it's bullish, bearish, or neutral - toward their block chain projects. Harnessing positive sentiment can bolster trust, generate enthusiasm, and attract potential investors and users while identifying and addressing negative sentiment enables proactive reputation management. Moreover, tracking sentiment trends over time aids in assessing the impact of marketing efforts, news events, or product launches on the digital asset's perception and market performance, allowing marketing teams to adapt strategies effectively and maintain a favorable brand image in this dynamic and highly competitive landscape. Present research would consider the textual and graphical sentiment of the user for classification. This would help in sales increment or understanding the taste of users. Present research considers the Digital assets of NFTMANIA that are present on the core chain. The market marketplace for the selling of such digital assets is Young Parrot which allows the sale and purchase of such assets. Thus present research would play a significant role in marketing digital assets that are managed on blockchain.