CharacterBot: AI-Powered TV Series Insights

Authors

  • Gautam Department of Computer Science and Engineering, HMR Institute of Technology And Management, GGSIPU, Delhi 110036 Author
  • Himanshu Shahoo Department of Computer Science and Engineering, HMR Institute of Technology And Management, GGSIPU, Delhi 110036 Author

DOI:

https://doi.org/10.21467/proceedings.7.6.18

Keywords:

Theme Classification, Character Networks, Text Classification

Abstract

CharacterBot is an AI-based system that processes TV series using natural language processing (NLP). The system automatically detects recurring themes, maps out character relationships, classifies text into meaningful categories, and offers an interactive chatbot that mimics TV characters.The primary goal of CharacterBot is to streamline narrative analysis for researchers and fans, providing deeper insights into plot dynamics and character interactions.

References

[1] F. Aslam and G. Yadav, “The impact of artificial intelligence on chatbot technology: A study on the current advancements and leading innovations,” Eur. J. Technol., vol. 7, no. 2, pp. 62–72, 2023.

[2] O. Schmitt and D. Buschek, “CharacterChat: Supporting the creation of fictional characters through conversation and progressive manifestation with a chatbot,” arXiv, Jun. 2021.

[3] M. H. Kurniawana et al., “A systematic review of AI‑powered chatbot intervention for managing chronic illness,” Ann. Med., vol. 56, no. 1, 2302980, 2024.

[4] M. Rana and M. Atique, “Language translation: Enhancing bi‑lingual machine translation approach using Python,” I‑Manager’s Journal on Computer Science, vol. 7, no. 2, pp. 36–42, Jun.–Aug. 2019, doi:10.26634/jcom.7.2.15597.

[5] G. Lample et al., “Neural architectures for named entity recognition,” in Proc. NAACL‑HLT, 2016, pp. 260–270.

[6] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre‑training of deep bidirectional transformers for language understanding,” in Proc. NAACL‑HLT, 2019, pp. 4171–4186.

[7] X. Zhang, J. Zhao, and Y. LeCun, “Character‑level convolutional networks for text classification,” arXiv, Sep. 2015.

[8] R. Lowe et al., “Towards an automatic Turing test: Learning to evaluate dialogue responses,” arXiv, Jan. 2018.

[9] K. Kowsari, K. Jafari Meimandi, M. Heidarysafa, S. Mendu, L. E. Barnes, and D. E. Brown, “Text classification algorithms: A survey,” Information, vol. 10, no. 4, Art. no. 150, Apr. 2019, doi:10.3390/info10040150.

[10] J. Li et al., “A persona‑based neural conversation model,” in Proc. ACL, 2016, pp. 994–1003.

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Published

2025-11-21

Issue

Vol. 7 No. 6 (2025): Proceedings of the 3rd International Conference on Artificial Intelligence, Machine Learning and Cybersecurity

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite

[1]
Gautam and H. Shahoo, “CharacterBot: AI-Powered TV Series Insights”, AIJR Proc., vol. 7, no. 6, pp. 147–154, Nov. 2025, doi: 10.21467/proceedings.7.6.18.