Advanced sentiment analysis in gaming: teaching AI slang, memes and sarcasm

AI is really great at recognising sarcasm. Wait, is that sarcasm? Automated sentiment analysis can’t always tell the difference, which is a problem when you really need to know not just what your players are saying, but what they really mean.

Gamers have their own way of communicating that can be confusing. Conversation among gaming communities is full of slang, memes and a heavy dose of sarcasm. While this fosters a vibrant and dynamic culture among the members in the know, it presents a significant challenge for automated sentiment analysis tools.

Understanding gamer communication is crucial for game studios and brands because it directly impacts their ability to engage with and retain players. Gamers are highly vocal and form strong opinions about games, updates and what companies get up to, often expressing themselves through slang, memes and sarcasm. If sentiment analysis tools misinterpret this feedback, companies risk taking the wrong actions – such as missing widespread frustration or assuming sarcasm is praise.

Traditional sentiment analysis methods struggle to grasp the nuances of gamer language, leading to misleading interpretations of player sentiment. If the analysis misinterpret nuances it will offer up inaccurate insights, which is a problem for game studios and brands looking to feel out the mood of their fans. Miscommunication can lead to poor decision-making around game development, updates, and marketing strategies, potentially damaging trust and loyalty within the community. 

On the other hand, accurately understanding player sentiment allows studios to stay ahead of potential issues, foster stronger relationships with their audience and create experiences that resonate with fans. This insight also supports better community moderation, faster response times to feedback, and overall improvements in player experience and satisfaction.

The good news is that AI can learn the ways of gamingspeak. By leveraging specialized lexicons and machine learning techniques, you can embrace the unique way your fans talk and fully understand your community.

The intricacies of gamer communication

To understand player sentiment, it’s essential to first grasp the unique language of gamers. Unlike everyday conversation, gaming communities use a lexicon filled with slang that has evolved from years of shared experiences. Let’s take a look at some common gaming slang and ways of talking, and why these intricacies can confuse traditional sentiment analysis tools.

Gaming slang

Gaming culture has birthed its own lexicon, with terms like “pwned” (a misspelling of “owned,” signifying total domination) and “noob” (a derogatory term for an inexperienced player). Some terms may be even harder to deal with because they have other meanings (known as “polysemy”) such as “camping” (which in gaming terms means staying in one spot to ambush rivals). Without contextual understanding, standard sentiment analysis tools may misclassify these expressions, misjudging the tone and intent of player discussions.

Memes in gaming

Memes are a core part of gaming culture, often combining images and text to express complex emotions or cultural references. Whether it’s an in-game screenshot paired with a funny caption or inside jokes tied to a game’s mechanics, memes require contextual analysis to determine their sentiment accurately. Similarly, emojis can add nuance or even reverse the meaning of the actual words a player uses.

Take the meme “Press F to pay respects”, which originates from a feature in Call of Duty: Advanced Warfare. It’s since become widely used in gaming communities to mock dramatic (or not so dramatic) situations. So if a player says, “My game crashed again… F,” sentiment analysis might see this as neutral or even respectful because of the phrase “pay respects”, whereas the player is actually expressing frustration in an ironic way.

Sarcasm and irony

Sarcasm is a staple in gaming discussions, often used to convey dissatisfaction or humour. The problem is that sarcasm and irony mean the opposite of what’s being said. For instance, a statement like “Great, another balanced update” might be misclassified as positive by traditional sentiment analysis tools due to the presence of positive words like “great.” Conversely, after an in-game mishap, a player might comment, “Best team ever, 10/10 would lose again!” Although sarcastic, this is a playful expression of amusement and camaraderie, rather than annoyance. Clearly, without an advanced understanding of what’s being said, companies might come away with exactly the opposite impression of what fans are feeling.

Challenges in traditional sentiment analysis

Context-dependent errors and sarcasm detection

Sentiment analysis tools that rely on simple keyword recognition often fail to interpret sarcasm and contextual meanings. A phrase such as “I love how my game crashes every five minutes” could be incorrectly tagged as positive unless the system understands the underlying sentiment.

Nuances in human language

Human language is fluid, incorporating idioms, cultural references, and ever-evolving slang. Language also includes phrases that work in combination to change the meaning of the individual words. For example, AI needs to take onto account the whole sentence rather than just individual words, and employ “Negation detection” when dealing with phrases like “not bad”: on the face of it, two negative words, but which combine across the sentence to make a positive. Ambiguous wording, qualitative statements and other complexities makes it difficult for automated sentiment analysis systems to provide accurate results without domain-specific enhancements.

Enhancing sentiment analysis with specialized lexicons

Lexicon-based sentiment analysis

One way of recognising meaning is to assign meaning to a predefined lists of words, setting out whether they’re positive or negative (this is known as “polarity”). But while lexicon-based sentiment analysis is effective for general sentiment assessment, they often lack domain specificity. In gaming, a word like “kill” might not automatically be negative. It could be neutral or even positive – for example, if a player is showing off their kill streak in a game. 

Domain-specific lexicons

Traditional sentiment analysis can misinterpret gaming slang and sarcasm, leading to inaccurate insights. So it’s important to develop lexicons tailored to gaming, addressing context-specific meanings. For example, with a gaming lexicon, terms like “buff” and “kill streak” won’t trigger negative classifications but instead reflect their intended meaning, which are likely to be neutral or positive. This helps ensure more accurate sentiment analysis of player feedback.

At the same time, gaming language is constantly evolving. New terms and memes emerge constantly. Without frequent updates, even a tailored lexicon can become outdated, leading to misclassification of newer expressions. For example, phrases like “nerfed to oblivion” (meaning a character or feature was significantly weakened) might not exist in older lexicons. Regular updates ensure sentiment analysis systems stay relevant and continue to provide accurate insights.

Machine learning approaches to decipher gamer language

Combining machine learning and lexicon-based techniques

Integrating machine learning with lexicon-based techniques can improve sentiment classification. Using lexicon outputs as input features for machine learning classifiers has been shown to improve accuracy, allowing systems to better interpret the sentiment of gamer interactions.

Deep learning for sarcasm detection

It is possible for automated systems to spot sarcasm. Deep learning models, which find patterns in vast datasets and “learn” independently, are beginning to do this. Examples include Convolutional Neural Networks (CNNs), which are good at analysing images, and Recurrent Neural Networks (RNNs), which are effective at understanding natural language.   CNNs are commonly used to detect patterns in structured data (like word relationships), while RNNs excel at analysing sequences of information, such as sentences, by remembering the context of previous words. These models enable AI to recognize subtle cues, making it more effective at detecting sarcasm and other nuances in gaming conversations.

Case studies and practical applications

Steam reviews analysis

Gaming platform Steam is a good place to start, because it’s full of reviews written by players. A high-performance DistilBERT-based sentiment analysis model fine-tuned on Steam reviews has demonstrated the ability to interpret gaming-specific language accurately. The people behind this system identify a number of uses: game recommendations based on user sentiment, ensuring suggestions align with player preferences; community management empowering admins and mods to quickly identify and address negative feedback; and market research.

League of Legends community sentiment analysis

Researchers have used sentiment analysis within the League of Legends community to identify and mitigate toxic behaviour among players, using Bag-of-Words and Weight Score method compared with machine learning techniques including Logistic Regression, Support Vector Machine (SVM) and CatBoost. By applying machine learning and specialised lexicons, developers have gained a better understanding of player sentiment, enabling them to improve in-game moderation and enhance user experience.

Implementing advanced sentiment analysis in gaming companies

Addressing sarcasm and contextual nuances

To implement effective sentiment analysis, gaming companies need to develop and adopt AI models capable of understanding context-dependent and differentiating between literal and implied meanings so they can assign accurate sentiment scores. It’s important to train these models on domain-specific data, so they remain accurate and relevant as gaming language evolves.

Leveraging AI-driven sentiment analysis

Manually keeping up with every conversation in a Discord server would be impossible. Automated tools do the hard work of gathering what’s important, so you have data to act on – and in real time, so you can quickly respond to changing moods. When interpreted accurately, these insights can inform game updates, community engagement strategies and customer support initiatives. This gives you the information you need to respond to what players actually want (and don’t want), boosting engagement in both the community and the game. 

Get started with Levellr

Accurately interpreting gamer lingo is essential to understand your fans. The answer is to invest in specialised real time sentiment analysis that just gets slang, memes, and sarcasm, so you can foster better player engagement and improve community management. Leveraging AI-powered sentiment analysis empowers developers, community managers, and analysts to collect meaningful insights from player communications and make data-driven decisions that enhance the overall gaming experience. 

At Levellr, we work with a range of clients, helping you drive tangible marketing ROI and monetisation value through meaningful emotional connection with fans. Levellr’s AI-driven platform helps executives turn fragmented community conversations into actionable insights, enabling teams to identify trends, track sentiment, and make data-backed decisions faster.

Contact Levellr today to discover how you can tap into the Discord opportunity to foster fan engagement, drive ROI and strengthen player loyalty.