Over the past decades, there has been an increasing concern about the prevalence of abusive and violent content in Hollywood movies. This study uses Large Language Models (LLMs) to explore the longitudinal abuse and sentiment analysis of Hollywood Oscar and blockbuster movie dialogues from 1950 to 2024. By employing fine-tuned LLMs, we analyze subtitles for over a thousand movies categorised into four genres to examine the trends and shifts in emotional and abusive content over the past seven decades. Our findings reveal significant temporal changes in movie dialogues, which reflect broader social and cultural influences. Overall, the emotional tendencies in the films are diverse, and the detection of abusive content also exhibits significant fluctuations. The results show a gradual rise in abusive content in recent decades, reflecting social norms and regulatory policy changes. Genres such as thrillers still present a higher frequency of abusive content that emphasises the ongoing narrative role of violence and conflict. At the same time, underlying positive emotions such as humour and optimism remain prevalent in most of the movies. Furthermore, the gradual increase of abusive content in movie dialogues has been significant over the last two decades, where Oscar-nominated movies overtook the top ten blockbusters.

The cold start problem is a common challenge in various domains, including media use cases such as predicting viewership for newly launched shows on Over-The-Top (OTT) platforms. In this study, we propose a generic approach to tackle cold start problems by leveraging metadata and employing multi-model ensemble techniques. Our methodology includes feature engineering, model selection, and an ensemble approach based on a weighted average of predictions. The performance of our proposed method is evaluated using various performance metrics. Our results indicate that the multi-model ensemble approach significantly improves prediction accuracy compared to individual models.

As a newbie to the K-drama phenomenon, the first one I watched on the recommendation of friends and family was Squid Games last year. The next thing I knew, the OTT platform I was on showed recommendations to several K-dramas and even similar shows in my local language. With new shows on my list, in Korean and local languages, I had fortunately bucked the 2021 trend of peak COVID boredom. I was making full use of my subscription expenses with new and highly relevant content areas to explore. AI was at play, and in ways that really addressed my needs as a consumer.

OTT platforms are now the primary entertainment source for a lot of people. You want to watch a movie, catch up on your favorite TV show, or just kick back and support your sports team -- you can access any content more conveniently via streaming services. There is a ton of effort that goes into keeping all of those users engaged with the OTT platform. Apart from delivering the best content, the platforms have to think about ways to make the viewing experience the most convenient. So, let's talk about just that -- you can enhance user engagement by providing a better viewing experience with cognitive computing. Ever find yourself looking at the credits when the movie or a TV series episode is over?

The recommendation engine has become quite popular across diverse industries in recent years. The recommendation engine is gaining rapid traction from OTT (Over the Top) platforms to e-commerce stores. Whether you have just started your OTT platform or plan to scale it up, recommendation engines can significantly improve your profitability. A Recommendation engine or recommendation system is an information filtering tool that provides the most relevant suggestions regarding products or services to various customers. A recommendation engine uses machine learning algorithms to collect and analyze user activities such as their preferences, search history, and others.

OTT platforms are becoming popular now than ever before. The competition between different OTT platforms has increased tremendously. Technologies like AI for OTT services can help businesses stay relevant in this cut-throat competition. According to a report, by the end of 2019, about 182.5 million people in the US will view content via OTT services. This represents more than 50% of the US population and provides huge business opportunities for OTT service providers.

Why is content personalization important? OTT (Over the Top) platforms are transforming the global entertainment scene. The critical players, like Hulu, Netflix, and Disney, are competing in terms of viewership and revenues. With the increasing overlap of content across all these platforms, it is crucial for these services to improve the consumer experience by delivering relevant and engaging content to prevent audience churn. Content personalization is, therefore, vital to acquire more viewing time and improve market share.

Over the Top (OTT) content distribution has changed the way video or audio content is consumed and it seems to stay for a while. Consumption of home entertainment via Internet-connected devices has increasingly become the trend for most people. Even though linear TV today continues to offer traditional TV packages along with OTT, viewing habits have shifted towards OTT-only content over the years. Studies suggest that if your platform is easily discoverable, if you have great content, if you offer an intuitive user experience, and if it is reasonably priced, people will subscribe and get hooked onto your OTT service. OTT customer acquisition and retention is quite a challenge, especially in a market with many players that offer original content, that are attractively priced, and that offer consistent user experience.