Sentiment Analysis to Improve Quality and Customer Satisfaction

Sentiment analysis is a technique used in marketing to analyze and interpret the emotions and opinions expressed by customers toward a brand, product, or service. It involves using natural language processing (NLP) and machine learning (ML) algorithms to identify and classify the sentiment of textual data such as social media posts, customer reviews, and feedback surveys.

Marketing insights through sentiment analysis

By performing sentiment analysis, marketers can gain insights into customer perceptions of their brand, identify areas for improvement, and make data-driven decisions to optimize their marketing strategies. For example, marketers can track the sentiment of customer reviews to identify which products or services are receiving positive or negative feedback and adjust their marketing messaging accordingly.

Overall, sentiment analysis is a valuable tool for marketers to understand customer sentiment, gauge customer satisfaction, and develop effective marketing campaigns that resonate with their target audience.

Sentiment analysis has been around for a while, so you might be wondering what ChatGPT could bring as added value. Besides the accuracy of the analysis (it being the most powerful model on the market right now), ChatGPT differentiates itself from other sentiment analysis tools since it is artificial general intelligence (AGI).

This means that when we use ChatGPT for sentiment analysis, we are not using one of its specific APIs for that task. The core idea behind ChatGPT and OpenAI models is that they can assist the user in many general tasks at once, interacting with a task and changing the scope of the analysis according to the user’s request.

Granular sentiment analysis with ChatGPT

So, for sure, ChatGPT is able to capture the sentiment of a given text, such as an X (Twitter) post or a product review. However, ChatGPT can also go further and assist in identifying specific aspects of a product or brand that are positively or negatively impacting the sentiment. For example, if customers consistently mention a particular feature of a product in a negative way, ChatGPT can highlight that feature as an area for improvement. Or, ChatGPT might be asked to generate a response to a particularly delicate review, keeping in mind the sentiment of the review and using it as context for the response. Moreover, it can generate reports that summarize all the negative and positive elements found in reviews or comments and cluster them into categories.

Let’s consider the following example. A customer has recently purchased a pair of shoes from our e-commerce company, RunFast, and left the following review.

Let’s ask ChatGPT to capture the sentiment of this review: