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Analysing player sentiment from reviews and social media to improve services

Introduction

The gambling industry is a highly innovative and rapidly developing branch that cannot function properly without the input from the users in their efforts to improve the quality of services offered. This makes it important to understand player sentiment, which can be extracted from reviews and social media content. With the integration of Natural Language Processing (NLP) AI, this process has been made easier and more effective. Natural Language Processing (NLP) is a subfield of artificial intelligence that deals with the interaction between human beings and machines using language that a person can easily understand, which makes it very useful in the gambling sector.

Challenges

There are several issues that the gambling industry encounters when it comes to identifying and processing players’ sentiment. First of all, the large amount of data available in the form of reviews and social media posts can be hard to capture and control. By manually analyzing the data, one is likely to make mistakes that can be avoided if done automatically. Secondly, understanding the sentiment from text data is not a straightforward task because language is a dynamic tool that can be used in different ways, including the use of sarcasm and irony. Third, it is imperative to address negative sentiment in a timely manner so as to minimize customer churn but this is not easily achievable without an effective system. Finally, there is the problem of how the industry can utilize the information generated from sentiment analysis in enhancing their services.

AI Solutions

This is where NLP AI comes in as a way of solving such issues. It is capable of managing large quantities of text information, and therefore identifying and classifying sentiments that players have. Some of the most sophisticated NLP AI algorithms can also decipher different language aspects making the sentiment analysis more precise. In addition, NLP AI can help companies to identify negative sentimenting real time basis as well. It can also link up with other structures to put into use the information that has been gathered from sentiment analysis. OpenAI and IBM Watson are some of the examples of the state-of-the-art companies in this field. For example, IBM Watson’s Tone Analyzer provides a linguistic analysis that helps in identifying emotions in text which can be beneficial for gambling companies.

Benefits

There are a lot of advantages of applying NLP AI in the gambling industry. This is because with better sentiment analysis, an organization is able to enhance its knowledge of its customers and in turn provide better services to them. This means that the time and resources that would have been used in sentiment analysis are reduced thus acting as a cost effective tool. Real-time alerts make it possible to act on negative sentiment before it becomes a threat of customer defection. Finally, the incorporation of findings from sentiment analysis into service enhancement processes helps in making better decisions and in the long run enhance the experience of the user.

Return on Investment

The ROI of applying NLP AI in the gambling industry is impressive. This paper shows that by enhancing customer satisfaction, organisations can enhance the loyalty of their customers and in turn their revenue. Other costs that can be saved include the time and resources used in analyzing sentiment also help to enhance the ROI. In addition, stopping customer defection is a financial benefit that can be very valuable. As has been mentioned, the ROI will differ with the particular case of each company and yet, it is perceivable that the advantages of NLP AI dominate the disadvantages.

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