Story understanding and analysis have long been challenging areas within Natural Language Understanding. Automated narrative analysis requires deep computational semantic representations along with syntactic processing. Moreover, the large volume of narrative data demands automated semantic analysis and computational learning rather than manual analytical approaches. In this paper, we propose a framework that analyzes the sentiment arcs of movie scripts and performs extended analysis related to the context of the characters involved. The framework enables the extraction of high-level and low-level concepts conveyed through the narrative. Using dictionary-based sentiment analysis, our approach applies a custom lexicon built with the LabMTsimple storylab module. The custom lexicon is based on the Valence, Arousal, and Dominance scores from the NRC-VAD dataset. Furthermore, the framework advances the analysis by clustering similar sentiment plots using Wards hierarchical clustering technique. Experimental evaluation on a movie dataset shows that the resulting analysis is helpful to consumers and readers when selecting a narrative or story.

The emotional arc is a universal narrative structure underlying stories across cultures and media -- an idea central to structuralist narratology, often encapsulated in the phrase "all stories are one story." We present a framework for procedural game narrative generation that incorporates emotional arcs as a structural backbone for both story progression and gameplay dynamics. Leveraging established narratological theories and large-scale empirical analyses, we focus on two core emotional patterns -- Rise and Fall -- to guide the generation of branching story graphs. Each story node is automatically populated with characters, items, and gameplay-relevant attributes (e.g., health, attack), with difficulty adjusted according to the emotional trajectory. Implemented in a prototype action role-playing game (ARPG), our system demonstrates how emotional arcs can be operationalized using large language models (LLMs) and adaptive entity generation. Evaluation through player ratings, interviews, and sentiment analysis shows that emotional arc integration significantly enhances engagement, narrative coherence, and emotional impact. These results highlight the potential of emotionally structured procedural generation for advancing interactive storytelling for games.

While humans are still struggling to understand each other, machine learning can now manipulate and control their emotions. Artificial intelligence (AI) is advancing rapidly and trying to answer the common query of "whether machines can be intelligent without any emotions?" Machines have started interacting with humans in a human-like fashion. No wonder, AI technologies are being used in various areas, be it filtering applicants for a job, targeting the ideal customers for an advertisement, regulating the traffic flow, or anything else. Machine learning enhances its interaction with humans by helping brands obtain emotional insights in real-time, which helps marketers to gain profit.

Sunspring debuted at the SCI-FI LONDON film festival in 2016. Set in a dystopian world with mass unemployment, the movie attracted many fans, with one viewer describing it as amusing but strange. But the most notable aspect of the film involves its creation: an artificial-intelligence (AI) bot wrote Sunspring's screenplay. "Maybe machines will replace human storytellers, just like self-driving cars could take over the roads." A closer look at Sunspring might raise some doubts, however.

On the surface, The Godfather, The Sixth Sense and Little Miss Sunshine appear to have little in common. But even though these films belong to different genres and have very different plots, technically they have the same "emotional arc" - a journey of highs and lows. Using artificial intelligence, we analysed more than 6,000 scripts from the past 80 years and discovered all films fall within six emotional arcs. These include the emotional rise of "rags to riches" films such as The Shawshank Redemption and the rise and fall of "man in a hole" films such as Who Framed Roger Rabbit. But which of these are the most successful, critically and commercially? Tragedies, which depict a continuing emotional fall, appear to receive the highest number of Oscar nominations per film.

Smart machines are giving storytellers and risk managers alike a helping hand. Burgeoning data analyzed by ever more intelligent machines are opening pathways to surprising applications and providing solutions to problems that have been out of reach. In the film industry, machines "watch" movies and videos, charting their emotional intensity and giving content creators clues about to how to make stories more appealing. And in banking, AI's ability to detect anomalies among millions of transactions helps bank risk officers eliminate false positives that are a drain on productivity. For a growing number of industries, AI is tilting the playing field--you'll need to understand how before your competitors do. Machine-learning models can help screenwriters and directors fine-tune scripts and imagery.

In today's time, technology has led to a diverse change in the world from how we live to our way of communication. Artificial Intelligence (AI) is one of them which has improved the efficiency in our lives that many of us didn't even notice. With the emergence of this era where unique and real storytelling is valued more than ever, AI can be a powerful tool for publishers, brands and anyone else who aims to create engaging content in a sustainable, consistent and scalable way. Earlier there was a big question mark whether a computer can write a great novel or a script for a movie? However, AI is manna from heaven for sci-fi writers. There is a sentient computer called Heuristically Programmed Algorithmic Computer (HAL) wreak quiet havoc in 2001: A space odyssey.

Can a computer write a great novel or a script for a movie? Artificial intelligence (AI) is manna from heaven for sci-fi writers. We've seen a sentient computer called HAL wreak quiet havoc in 2001: A Space Odyssey. We've watched a robot girl's will to survive in 2015's Ex Machina. Most recently we've seen an AI-meets-the-wild-west scenario in TV series Westworld.

Computers don't cry during sad stories, but they can tell when we will. Sunspring debuted at the SCI-FI LONDON film festival in 2016. Set in a dystopian world with mass unemployment, the movie attracted many fans, with one viewer describing it as amusing but strange. But the most notable aspect of the film involves its creation: an artificial-intelligence (AI) bot wrote Sunspring's screenplay. "Maybe machines will replace human storytellers, just like self-driving cars could take over the roads."

Artificial intelligence (AI) may not be ready to write the next blockbuster movie, but a team of AI researchers from the Massachusetts Institute of Technology's (MIT) Media Lab successfully used machine learning to teach computers about emotional arcs in movies. The researchers, which collaborated for this project with McKinsey, used machine learning to analyze thousands of videos, including movies, TV shows and short films found on Vimeo. "We developed machine-learning models that rely on deep neural networks to'watch' small slices of video--movies, TV, and short online features--and estimate their positive or negative emotional content by the second," the team wrote in a blog post Monday morning. The approach didn't just pay attention to the general plot line of a movie, but also to more subtle aspects, including the score, and close-ups of a person's face. Using these clues, the project's machine learning algorithms were able to identify positive and negative emotions, and map out the extend to which each scene would provoke emotional responses -- something the researchers called "visual valence."