Creative company It appears today as a fresh type of intelligence company. Uses AI to analyze sentiments 1.5 million conversations about the best game publishers and their titles.
This means that he uses artificial intelligence to find out what players think about the 17 best game publishers – with insights produced by machine learning. Ai Creativ was able to analyze over 1.5 million online calls in Reddit, YouTube, Discord and Press in six months. It took about 10 days. The company has undergone around 9,300 sources of messages and sentiments about game publishers. Then he conducted his analysis of the study, which covered the period from November 1, 2024 to the end of April 2025.
I talked to Creativ CEO Wesem Morton, Cto Joe Lai and Coio (operational and information director) Vibhu Bhan about the results. Here is some information from exclusive analysis.
“We are called an intelligent marketing company. And essentially the reason why we conduct the study is that we discover how to perform consumer insights using LLM,” said Morton. “The study is literally the consumption of a million and a half consumer conversations about these various publishers.”
By distinguishing a “analysis of sentiments”, the goal is to find out what players think about game companies based on what players say on social media. Bhan said that a model with a huge AI (LLM) language is trained to detect sarcasm, a slang specific for the game and subsequent nuances.
“True innovation here is a better understanding of the context and slang, so the analysis of sentiments is much more contextual, not just the result,” said Bhan. “If you look at the traditional sentiment analysis, he looks at the existence of some words. But the language is complex.”
The analysis of sentiments has appeared in recent years as a way to understand Zeitgeist around the game or company. But often the analysis suffered because the analysis used did not really understand the players or their comments on topics. Now, in the case of LLM, Morton said that machine learning understands sophisticated nuances and does a better job on a larger number of data that he can consume.
The company consumes data and then invents the results of sentiments about game publishers to see what they did to aid or hurt their brand in conversation with players. Older reports can find out how often a set of words was used (like the name of the game or company). But this often did not have the opportunity to understand the full context of the discussion about games, and then the correct summary. But LLM is better to understand the context around a huge amount of data.
“The context becomes much more important, because this allows you to understand the direction of sentiment, because there may be several entities in the sentence. And the second thing is the switch that we do because of sarcasm, which is seen as a positive false, when it has a negative reaction,” said Lai.
Lai said LLM has a better ability to understand the context of the language.
“The beauty of LLMS is that we are able to collect and train our models on this game data,” said Lai. “We are able to train models to be able to detect an information line that appears for each of these games, and if they are used in a positive or negative way.”
The biggest topics of the conversation
One thing that LLM chose was that players had powerful opinions about exclusions and whether the owner of the platform should keep the best game or transfer this game to other platforms to generate more sales. Fans who invested their money in a specific console did not like it.
The biggest topics of the conversation included the monetization of games, franchise, game platforms, exclusive and consolidation of the industry and corporation. After monetization, players rewarded open communication to principles and studies that avoid monetization models that affect the gameplay and mechanics. It was the widest trend in the data set, and consumers perceive Activision Blizzard, Ubisoft, EA, Amazon, Netease, Evolution Gaming and Roblox as particularly bad criminals of bad moneting practices.
In addition, LLM captures conversations that naturally happen. However, the study puts the player in the ambulance that they are surveyed for their opinions. This player may think about whether to answer truthfully or not, based on what they think the study researcher wants to hear.
How companies did
Netflix did not have many stories as a game publisher, and his mobile games were not gigantic hits yet. This helps to explain why he has a negative result from players. Some of the sentiments are happening around the game, just like the NBA game, but many of them happen outside of playing in social media.
Morton said that games receive a huge enhance in Hollywood awareness, because films based on games such as Minecraft movie and The Last of Us television program receive high ratings and reach more people who do not know about games.
“The nice part of this technology is that you can specifically drill on what makes people happy, sad,” said Morton.
Activision Blizzard had many talks at World of Warcraft. But many players were also not fans of how the company coped with the transition from Overwatch to Overwatch 2. Ubisoft also came out with the worst result of all the publishers of the game, but it was not clear why. A lot of discussions about the character Assassin’s Creed: Shadows. But this game received positive reviews as opposed to previous games, such as Star Wars: Outlaws and Skull & Bones.
In this study, the company did not focus on any specific game. But it can do it in the future.
In the case of LLM, the test can be performed within 10 days compared to weeks for other methods. Morton said LLM can only absorb, consume and process data faster, but can analyze much more data and much faster. Over time, the analysis can become much more detailed, focusing on the characters of any game or other details. This analysis can give the team a chance to turn to another character if he has a negative result.