Today social media has become a powerful platform for expressing emotions online regarding a brand or a company. It is convenient for customers to express their feelings and perceptions regarding the brand, whether it’s a positive or negative view.   

Businesses and brands can analyze these reviews to get a better idea of their products or services and treat such feedback as a scope for improvement.   

This whole process of extracting emotions from the human text is called sentiment analysis. Nowadays, sentiment analysis tools are considered extremely powerful for understanding the view of customers and discovering their satisfaction level.  

This process of analyzing sentiments can be time-consuming, and generative AI can analyze the sentiments in a fraction of the time, making the outcome reliable. AI can understand and interpret human emotions that are expressed in textual data.   

Let’s delve into the application of Sentiment analysis with generative AI –  

Customer Support 

Whenever customers have any negative feedback regarding the service, they want to shed their emotions and frustration. In such a scenario, not getting a prompt reply will escalate the frustration and will create a negative branding of the company. Adopting chatbots powered by generative AI can understand the human text and respond to queries promptly including emphatic responses.   

Market Research  

To explore and find new customer opportunities, generative AI can be adopted to dive into valuable insights regarding consumer opinions and their preferences. It can be fruitful for companies going to launch a new product or for making changes in their existing product basket. Generative AI can go through vast amounts of data to identify ongoing trends and related sentiments. Real-time analysis helps businesses stay ahead of the competition and adjust their strategies to meet dynamic consumer expectations.   

Finance and Investment decisions  

Finance industries can analyze market sentiments and make informed investment decisions, all thanks to generative AI. Investors using AI can analyze news articles and financial documents to have an idea of the sentiments of customers. Additionally, sentiment analysis helps in predicting risks. Positive sentiment indicates potential growth, while negative indicates risks of not investing in a particular stock. Thus, by evaluating opinions and emotions, companies can take proactive actions.   

Hospitality Sector  

Analyzing public reviews and social media handles can help detect customer satisfaction levels of the hotels. It would facilitate the hospitality sector in identifying if there is any room for improvement in the services or offerings, enhancing the overall customer experience. Generative AI can also be used to identify public sentiment toward a specific destination, helping tourism marketing and development.  

Healthcare Sector  

To address patient concerns proactively, generative AI aids in analyzing reviews and feedback, ultimately contributing to boosting patient experience. Additionally, if any patient is not satisfied with services, it can also be tracked with sentiment analysis algorithms backed with generative AI. Thus, healthcare providers can address the concerns preventing further escalating issues and offer quality care to patients.    

Summing Up  

Artificial Intelligence has undoubtedly brought a transformative change for any industry and sector. The power of generative AI models to interpret human text, analyze hidden sentiment, and mimic human-like responses is truly a great deal for businesses.  

At Canopus Infosystems, we understand the need for robust automation, and the incorporation of artificial intelligence in enterprise applications will ease operations and boost efficiency. Our experts will help you leverage AI and make more informed decisions.


2 mins read


Gaurav Goyal

He is the Chief Technical Officer and Co-Founder at Canopus Infosystems Pvt Ltd. He completed his graduation in Computer Programming in 2003 and has experience in managing data science teams, quantitative research, and algorithmic trading. He’s a proven track record in specialties like robust statistics, machine learning, large data analytics... with excellence and delivered 500+ projects to 200+ clients with his teams.

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