Taimur Khan, Ramoza Ahsan and Mohib Hameed, FAST National University of Computer and Emerging Science, Pakistan

Story understanding and analysis have been a challenging domain of Natural Language Understanding. The need for automated narrative analysis demands deep computational semantic captures, along with the syntactic analysis of the text. Moreover, a large amount of narrative data requires automated semantic analysis and computational learning rather than manual approaches to the analytical tasks. In this paper, we propose a framework that analyzes the sentiment arcs of movie scripts and performs an extended analysis regarding the context of the characters involved in the movie. The framework enables us to extract high and low concepts being delivered through the narrative. Using the methodologies of dictionary-based sentiment analysis, our proposed framework proceeded with a custom lexicon based sentiment analysis using Lab MT simple story lab module. The custom lexicon is based upon the Valence, Arousal, and Dominance scores (NRC-VAD lexicon). Furthermore, the framework advances the analysis by clustering similar sentiment plots using Ward’s hierarchical clustering technique. Our experimental evaluation using movie dataset demonstrates that the retrieved analysis is helpful to consumers and readers during the selection of a Narrative/Sto

Sentiment Analysis, Story Analysis, Natural Language Processing, Information Retrieval