The Role of Sentiment Analysis in Real-time Feedback Processing

MX Bites / August 25, 2023

Do you also find it fascinating how big businesses increase customer satisfaction despite managing several customers? This can only be possible with the input of Artificial intelligence. Consider the volume of reviews for a given product or service that floods the internet daily. It’s too much to comprehend.  The emotions underlying the feedback are even more difficult to interpret.  

For every piece of feedback that accompanies a product, service, or brand, the most important thing about the feedback is the sentiment. It carries the weight of the emotions in the texts and is also a window into customer’s behaviors. With an increase in the establishment of e-commerce and online businesses, which usually get a lot of reviews on social media, priority should be placed on sentiment analysis in feedback processing using artificial intelligence. Also know that the traditional method of scheming through customer feedback and guessing what the sentiments are may seem economical but will not bring about customer satisfaction in the end. Meeting customer expectations requires making alliances with the new system of feedback processing and interpretation of customer needs.

The rest of this article gives more insights into sentiment analysis, its importance, and why it should be prioritized in feedback processing.

What is Sentiment Analysis?

Sentiment analysis, also known as opinion mining, is the process of determining the emotional tone of a text or speech. It is a branch of natural language processing (NLP) that employs machine learning to find and categorize arbitrary textual content. Insights from consumer reviews, social media posts, and other text data sources can be gleaned via sentiment analysis.

Sentiment analysis follows a very holistic approach. Using a sentiment analysis tool, it takes the texts in their raw, uncategorized state as its primary data source and breaks them down into individual words or phrases. Then the sentiment analysis algorithm analyzes the processed text to determine the sentiment expressed. The sentiments are then characterized as follows based on the emotions they express:

    • Positive Sentiment: If the analysis finds that the language expresses a positive sentiment, it may be used to express acceptance, contentment, or positivity towards a specific product or service.
    • Negative Sentiment: Contrarily, if the analysis identifies a negative sentiment, it denotes dissatisfaction, criticism, or hostility against the object of the analysis.
    • Neutral Sentiment: Texts with an unclear positive or negative attitude are categorized as having a neutral sentiment.

In addition to the categorized sentiments above, some tools may also provide a sentiment score to estimate the intensity of the emotions.  If two customers purchase a product and give reviews on it, they can both be happy about the product, but to different degrees. Providing a sentiment score helps identify the extent of customer satisfaction and dissatisfaction.

Understanding the Importance of Sentiment Analysis in Real-time Feedback Processing

Real-time feedback is the process of collecting information or opinions immediately after they are shared on social media, through reviews, or on other platforms where people can freely air their opinions without fear of judgment. As opposed to surveys and feedback forms, real-time feedback is known to carry more genuine feelings from customers.

Therefore, one can accurately refer to real-time feedback as a goldmine where valuable business insights are hidden. When sentiments in feedback are analyzed, companies can use the insights gleaned from the sentiment score to improve brand strategies, improve customer satisfaction, and increase brand awareness. It also facilitates better decision-making. When real-time feedback is properly analyzed, companies can see more clearly how to incorporate better strategies into the next stage of their business plan. On occasions when companies are meeting customer expectations, analyzing the sentiment score could be an encouragement to them and help them know what aspect of their business is more important to the public and is likely to earn them global recognition.

What motivates businesses to act is the realization that customers want to be heard and have their emotions recognized. When this is an established fact in companies, they are apt to invest in getting real-time feedback and sentiment analysis using AI tools. 


We are in the era of data-driven decision-making, and sentiment analysis has proven to be the most important part of processing real-time feedback data. This is due to the fact that it explicitly expresses the feelings and opinions of customers in the degree to which they are communicated. This then informs businesses of the tactics to follow in order to maintain or improve customer satisfaction. It can also be used to inspire businesses and help them determine where to focus their efforts.

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