Did you know that the video game sector is larger than the movie and music industries combined? Interestingly, the sector is only growing. Almost 26% of the world population is playing some kind of video game. In turn, making the sector one of the most profitable in the entertainment industry.
To keep up with the profits, the video game industry is investing greatly in user reviews. As Bill Gates once quoted “Your most unhappy customers are your greatest source of learning.”
Incorporating the ideology, a successful game studio must know its customers and what they think about the game. Hence, these insights play a central role in key decision-making while developing the game.
Role of AI in Sentiment Analysis
In a nutshell, sentiment analysis is the process of dividing reviews and feedback into positive and negative categories. In a more technical definition, Sentiment Analysis (SA) is an automated process that uses Artificial Intelligence to analyze textual content and identify sentiments and opinions. Brands use this to understand the sentiment of their customers in an aim to enhance the product.
With the recent advances in machine learning, the ability of algorithms to classify text has improved considerably. As you read along, you will discover how the algorithms are classifying the reviews and feedback data with an aim to better the customer experience.
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Data Acquisition
As the first step, the data is acquired. There are two popular ways to acquire data on the internet.
- Web scraping, where automation is used to acquire data from websites mechanically.
- Application Programming Interfaces (APIs), some websites allow the users to scrap their predefined data in exchange for user data.
Data Cleaning
Once the data is in place, the system performs the polarity annotation, preprocessing, and feature extraction. These steps serve as a crucial step in preparing the data for analysis.
- Polarity annotation helps the system identify whether the news, reviews, complaint, or suggestions are positive or negative.
- Pre-processing stage signifies that the system removes unnecessary data from the raw data.
- Feature extraction intends to transform raw information from the dataset into useful data supported by the classifiers.
Classifiers serve as the building block of the system. As a matter of fact, it is an algorithm that automatically orders or categorizes data into one or more of a set of “classes”.
Text Analysis and Prediction
Now, this is where things get really interesting. As you dive deep into the methods of the classifier algorithm, you will understand how does the system reaches the insight. One of the famous methods of a classifier is Random Forest. In fact, the algorithm combines the results of several decision trees, forming a cluster of the votes to make the prediction. The prediction insight is what a game creator could use to add or remove features in his latest game.
Payoffs of Sentiment Analysis
In a time when customer perception is shifting for video games. It is not just perceived as a leisure activity rather a supplement for brain development. For that reason, game development companies are betting on customer data such as players’ demographics, their opinion on the new updates, how long do they play, what time of the day are they active, who they play with, which levels are easier and similar metrics. The insights would likely help the companies evaluate the player personas, attributes that can cause players to disengage. As a result, improving the playability factor.
Presently, game development companies are leveraging the power of data and AI. The rise in revenue generation from the gaming industry is a testament to the growing popularity and quality of the games.
