Itamar Rogel

Mastering the game: Harnessing AI for powerful player sentiment analysis

Are you looking to better understand your players’ needs and wants in the gaming community? Well, you’re in luck! Managing a video game’s player community can be super exciting, but also a bit challenging, especially when dealing with millions of players. That’s where AI solutions come in handy, allowing gaming companies to analyze their players’ sentiments and make informed decisions based on the data.

Mastering the game: Harnessing AI for powerful player sentiment analysis

Some of the challenges in managing large player communities can be:

  1. Scale: As the player count grows, so does the amount of data generated by player feedback. It becomes difficult to manage and analyze such large data sets manually.

  2. Diversity: The player community can be diverse, with varying opinions and feedback on different aspects of the game. Managing such a diverse community can be challenging, especially when the feedback is conflicting.

  3. Real-time response: Players expect immediate responses to their feedback and issues. As the player base grows, it becomes increasingly difficult for game studios to respond to every issue in real-time.

  4. Reputation management: With millions of players, it’s important to manage the reputation of the game studio. Negative sentiment from players can spread quickly and can lead to a decline in player count.

  5. Continuous engagement: Players expect continuous engagement and new content. It’s important to keep players engaged to retain them and prevent churn. However, creating new content and updates requires resources and investment.

But how exactly can you analyze player sentiment in gaming using AI-Driven technology? Let’s break it down into a few simple steps:


Step 1: Track and monitor player feedback


The first step is to combine all the player feedback from all your data sources. Player feedback from the open web includes social media platforms, review boards, player forums, sources like Discord, Steam and more. Other player feedback data sources include internal sources like ticketing systems, such as Helpshift, and forums.

Combining internal and external player feedback data sources can provide numerous benefits for video game studios. Here are some of the advantages:

  1. Comprehensive analysis: Combining internal and external feedback data can provide a more comprehensive analysis of player sentiment. Internal feedback data can provide insights that the less vocal players provide by submitting tickets or posting on the studio internal player forum. External feedback data can provide insights into the player experience, such as social player support, community management, and social media sentiment.

  2. Improved decision-making: By combining data from various sources, game studios can make informed decisions that are backed by data. This can lead to better game design decisions, more effective marketing campaigns, and improved player retention.

  3. More accurate player feedback: Combining internal and external feedback data can provide a more accurate representation of player sentiment.

  4. Greater customer satisfaction: By combining internal and external feedback data, game studios can identify pain points in the player experience and take corrective actions to improve them. This can lead to greater customer satisfaction and improved player retention.

  5. Competitive advantage: Game studios that combine internal and external feedback data can gain a competitive advantage over their peers. By having a better understanding of player sentiment, they can make more informed decisions and create games that better meet the needs of their players.

Overall, combining internal and external player feedback data sources can help video game studios create better games, improve player retention, and gain a competitive advantage in the market.

Step 2: Categorize feedback


AI categorizes player feedback by sentiment by using natural language processing (NLP) techniques. It involves analyzing the words and phrases used in the feedback to determine whether the sentiment expressed is positive, negative, or neutral.


The AI system looks for specific keywords and phrases that are commonly associated with positive or negative sentiment, such as “great game” or “terrible experience”. The system then assigns a sentiment score to each piece of feedback based on the keywords and phrases identified.


But what happens with phrases like “angry birds” in a popular game we all love, “I shot him” in First person shooter games and so on? Are those negative, neutral or positive for AI analysis?


You got it right! Sentiment analysis is not always straightforward, as people often express their opinions in complex and nuanced ways that can be difficult for machines to interpret accurately. Therefore, AI platforms use machine learning algorithms to improve the accuracy of their sentiment analysis over time by learning from large amounts of feedback data.


Step 3: Quantify feedback


Okay, so we already spoke about how the AI system first categorizes the player feedback as either positive, negative, or neutral. This is done by looking for certain words or phrases that indicate a positive or negative sentiment. For example, “I love this game” would be categorized as positive, while “I hate this game” would be categorized as negative. And if a comment is just like “meh” or “meh, it’s okay”, then it’s classified as neutral because the AI can’t decide if the player likes the game or not.


Next up, the AI quantifies the feedback by counting the number of comments in each sentiment category. This gives the video game studio an idea of how many players feel positively, negatively, or neutrally about a particular aspect of the game. Think of it like taking the temperature of the player community to see if they’re feeling hot or cold about something.


And there you have it, folks! With AI-driven platforms, video game studios can easily measure and understand player sentiment by categorizing and quantifying the feedback data. It’s like having your own personal sentiment analyzer that works 24/7 to keep you informed about how your players are feeling.


Step 4: Understand overall sentiment


With the data now quantified, companies can see their players’ overall sentiment in real-time. This includes feedback on a specific game, brand, Live Ops, marketing campaign, or any other relevant topic.


Understanding the overall sentiment of the player community can be extremely beneficial for video game studios! It allows gaming companies to keep a finger on the pulse of their players and stay up-to-date with their opinions and feelings.


Knowing how players feel about various aspects of a game or the brand in general allows the company to take quick action to address any negative sentiment and capitalize on positive sentiment. This helps the company to stay ahead of the curve and maintain a competitive edge in the market.


Additionally, understanding player sentiment can help game studios to make data-driven decisions about updates, upgrades, and new features. This can save time and resources by ensuring that development efforts are focused on the features that players actually want and value.


Step 5: Dive into specific sentiment issues


Once you’ve identified a player sentiment issue, there are several ways to deep dive into it and work towards a solution:

  1. Live search and filtering: Use an AI-Driven Player Feedback Analytics platform to conduct a live search and filter through mountains of data on the topic of interest. This allows you to gather insights and understand the root cause of the problem quickly. Your team doesn’t work with one? Talk to us!

  2. Identify the scope and severity of the issue: Quantify the feedback, analyze it over time, and compare it with competitors’ games player feedback. Understanding the overall sentiment on the issue and comparing it with historical data helps determine the severity of the issue and scope of its impact.

  3. Assess player sentiment during key events: Identify when the sentiment issues occur, whether during marketing campaigns or specific game updates. Assess if there is a correlation between these events and the sentiment. This analysis helps determine if the player sentiment issue is temporary or if it’s a systemic issue that needs addressing.

  4. Pinpoint areas for improvement: Using data-driven insights, identify specific aspects of the game that players are unhappy with, such as game mechanics, graphics, or character design. Prioritize and plan for improvements that address these concerns, balancing player needs with your development team’s capabilities.

Don’t forget! Once the cause of the negative feedback is fixed, it’s important to communicate the solution to the player community through the game’s forums, social media channels, and email newsletters. Players appreciate transparency and value updates on the progress of issues they reported.


Congratulations, you’ve made it to the end of the article! By now, you should have a good understanding of how AI-driven player feedback analytics platforms can help video game studios analyze their player feedback and sentiment. With these platforms, you can track and monitor player comments and conversations, categorize feedback by sentiment, quantify feedback, and deep dive into specific sentiment issues. By taking advantage of these powerful tools, you can better understand your player community and make data-driven decisions to improve your games. So what are you waiting for? Give it a try and see the results for yourself!