Personalized Multilingual Novel and Story Creation Using GPT-2 with Sentiment Insights
This initiative presents a groundbreaking AI-enabled platform focused on tailored story and novel development, boasting strong multilingual capabilities. It empowers users to create distinct narratives in various languages, improving both accessibility and engagement. Thus, these features allow users to develop stories and novels with diverse emotional tones, plot dynamics, and characters to fit their unique requirements. A significant element of the platform is its incorporation of QR code sharing, which enables users to easily distribute their created content. This feature enhances the shareability of the stories and novels, broadening their audience reach and making them accessible to more people. Beyond content creation, the system offers tailored book suggestions based on the user’s chosen genre and theme. This recommendation system assists users in discovering new literary adventures that resonate with their preferences, further augmenting the platform’s appeal. To ensure the content’s quality and authenticity, sentiment analysis tools are utilized to assess the emotional tone and impact of the stories. This guarantees that the material fulfills the emotional needs of its target audience. A plagiarism detection feature is included to preserve originality, avoid content duplication and build trust in the platform’s reliability. By merging state-of-the-art technology with creative storytelling, this all-encompassing tool provides an engaging and secure space for users to unleash their creativity. The platform also encourages linguistic variety, inviting users from different cultural backgrounds to engage in and contribute to the storytelling process. The results of this project include a wide array of literary components and analytical evaluations, such as genre identification, thematic correlation, content type categorization, and accurate word count monitoring.
Authors
- Suresh Kumar Natarajan
- Shaik Ismail
- I Ambika
- Rajeshwar Voleti
- Nitish Shailendra Avasarala
- Kalava Bandhav
Institutions
- Jain University (IN)
Publication Details
- Published
- 2025-05-09
- DOI
- https://doi.org/10.1109/ginotech63460.2025.11076863
- Citations
- 1
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 2.48