What Are the Key Components of Effective Sentiment Analysis Algorithms?

May 24, 2024
May 24, 2024

Players of AAA multiplayer games generate about a terabyte of behavioral data daily. This data provides a treasure trove of information for game developers and marketers, but the raw data isn’t very useful. Sentiment analysis algorithms help developers and marketers turn massive amounts of data into useful insights.

Key Takeaways:

  • Sentiment analysis algorithms are tools that use natural language processing and machine learning to determine whether data is positive, neutral, or negative.
  • Sentiment analysis algorithms are useful for processing customer data efficiently, performing real-time analysis, and making sentiment analysis more consistent.
  • The primary components of sentiment analysis algorithms are customer data, polarity categories, emotion detection, aspect-based classifiers, intent analysis, language classifiers, rules, and machine learning classifiers.
  • Not all sentiment analysis algorithms will use all of these components or use them in the same way. You can adapt your algorithms to suit your goals and business needs.

What Are Sentiment Analysis Algorithms?

Sentiment analysis algorithms are tools that use natural language processing and machine learning to determine whether data is positive, neutral, or negative. Game developers and marketers can use this information to analyze how players feel about their games and their brands and identify opportunities to improve customer satisfaction.

Example of positive, neutral, and negative sentiment analysis
https://monkeylearn.com/sentiment-analysis/

Why Use Sentiment Analysis Algorithms?

Sentiment analysis algorithms provide a way to automatically analyze customer data. This has multiple benefits for game developers and marketers.

Processes Customer Data Efficiently

The game “Candy Crush Saga” has more than 3 million reviews on the Apple App Store and over 180,000 followers on X. Manually reading every review and social media conversation would require a substantial investment in labor hours.

Sentiment analysis algorithms can process and sort through reviews, social media conversations, and other communications much faster than humans. These algorithms can also efficiently classify the polarity, emotional content, and intent of those conversations.

This allows developers and marketers to turn customer data into actionable insights faster and at a lower cost. They can then use the resources they would have spent on reading customer communications on other projects.

Provides Real-Time Analysis

Performance issues, such as lag, crashes, or bugs, may frustrate players and cause them to stop playing your game. Similarly, when players are unhappy with gameplay features, game balance, disruptive players, or other elements of your game, they may churn if you don’t respond quickly.

The faster you can fix problems when they occur, the better your retention metrics will be. Sentiment analysis helps identify issues in real time so you can address them immediately.

Creates Consistency

Since the invention of textual communication, such as social media, or in-game messaging systems, developers have worked to provide ways to make it easier for users to accurately convey the sentiment of their communication. Tools such as emojis and emote wheels help game players clarify what they mean when communicating in the game world.

Without these kinds of tools, it is easy for two people to examine the same piece of data and

come to a different conclusion about its sentiment. Using algorithms ensures that you apply the same set of criteria to every piece of data, creating a more consistent analysis.

What Are the Components of Sentiment Analysis Algorithms?

Sentiment analysis algorithms consist of several key components. The exact components may vary based on the needs of your organization.

Customer Data

Customer data includes customer support correspondence, customer reviews, conversations on social media, and feedback surveys. It also includes conversations on forums and in-game messaging systems.

Polarity Categories

Polarity categories help you organize data into positive, neutral, or negative sentiments. The exact categories vary depending on the needs of the user. For example, if you just want to know whether data is positive, negative, or neutral, you could classify data into those three categories.

However, if you need to know more precisely how positive or negative a sentiment is, you could expand the polarity categories to include different levels. For example, you could use positive, very positive, neutral, negative, and very negative categories.

Emotion Detection

Emotion detection adds specific emotions to the polarity analysis. For example, it can detect happiness, anger, sadness, or frustration.

To identify these emotions, sentiment analysis algorithms may use lexicons, which are lists of words and the emotions they indicate, or complex machine learning algorithms. Some algorithms may use both.

Aspect-Based Classifiers

Aspect-based classifiers identify the specific aspect of a game or brand that a sentiment attaches to. For example, if a player wrote a review that said, “The story was good, but the graphics were ugly,” the algorithm would attach a positive sentiment to the game’s story but a negative one to the game’s graphics. This helps you focus on the specific elements of your game or brand that players like or don’t like.

Intent Analysis

Intent analysis identifies what the user’s intent is. For example, the player might intend to uninstall the app, research a product, or purchase an in-game item. This information helps you better target your marketing campaigns and assists with churn prevention.

Language Classifiers

Many games appeal to players who speak different languages. Language classifiers in sentiment analysis algorithms detect sentiment in textual data produced in languages other than your primary language.

Rules

Data analysts create a set of rules to help algorithms identify sentiment. These rules may include natural language processing techniques such as stemming, part-of-speech tagging, parsing, tokenization, and lexicons. The downside of rules is that they require regular maintenance and don’t account for how users combine words in sentences.

Machine Learning Classifiers

Data analysts may use machine learning classifiers instead of or in conjunction with rules. Machine learning classifiers use machine learning to associate data with specific sentiments.

Analysts must train models to associate specific data with the correct sentiments. The process may involve linear regression, support vector machines, naive Bayes, and RNN derivatives.

Flowchart example of the steps involved in training a sentiment analysis classifier
https://itechindia.co/us/blog/which-of-the-3-algorithms-models-should-you-choose-for-sentiment-analysis-2/

How Does Sonamine Help With Sentiment Analysis Algorithms?

Whether you are new to using sentiment analysis algorithms or are looking to get more out of your existing data analysis, the team at Sonamine can help. We can work with your in-house data teams or use our extensive data analysis knowledge to help you get started. Contact us today to learn more about our services.

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