Sentiment analysis models focus on polarity (positive, negative, neutral) but also on feelings and emotions (angry, happy, sad, etc), urgency (urgent, not urgent) and even intentions (interested v. not interested).

Sentiment analysis and natural language processing can reveal opportunities to improve customer experiences, reduce employee turnover, build better products, and more.

You can analyze text on different levels of detail, and the detail level depends on your goals. For example, you may define an average emotional tone of a group of reviews to know what percentage of customers liked your new clothing collection. If you need to know what visitors like or dislike about a specific garment and why, or whether they compare it with similar items by other brands, you’ll need to analyze each review sentence with a focus on specific aspects and use or specific keywords.

Why is Sentiment Analysis important?

Automated Sentiment Analysis is essential for properly understanding and quantifying the opinions expressed in the text. With large amounts of data, understanding the feedback in any meaningful way becomes time-consuming and expensive. On an Internet-wide scale, resorting to manual categorization is impossible.

By monitoring attitudes and opinions about products, services, or even customer support effectiveness continuously, brands are able to detect subtle shifts in opinions and adapt readily to meet the changing needs of their audience.

For online data, the insight lies in how people online are talking about your brand. For proprietary data, such as customer satisfaction or employee satisfaction reviews, the key business insight is in properly gauging the satisfaction level of respondents.

Most popular applications of sentiment analysis in real life

Social media monitoring

Social media posts often present some of the most truthful points of view about products, services, and businesses because users offer their opinions unsolicited. They are simply compelled to tell the world how they feel. Whichever industry you work in retail, finance, tech, health, and government you probably receive a lot of feedback on social media. And, you’re looking at hours, maybe even days, to process all that data manually.

But, with the help of machine learning software, you can wade through all that data in minutes, to analyze individual emotions and overall public sentiment on every social platform.

Product Analytics

Using sentiment analysis to look at product analytics can help your company keep an eye on what’s working and what’s not.

By segmenting certain features of your product through analysis, you can create marketing campaigns to target certain groups who may have shown interest in that specific feature. Or better yet, use their sentiment to change a feature that you thought was great but the customer actually hates.

The best part about tracking product analytics is that when customers give feedback, they really want to give it. They may mention certain additions to a product that you hadn’t thought of that they would love to see.

Improving Your Customer Support

Customer support management presents many challenges due to the sheer number of requests, varied topics, and diverse branches within a company not to mention the urgency of any given request.

Sentiment analysis with natural language understanding (NLU) reads regular human language for meaning, emotion, tone, and more, to understand customer requests, just as a person would. You can automatically process customer support tickets, online chats, phone calls, and emails by sentiment, which might also indicate urgency and route to the appropriate team.

Sentiment analysis can automatically mark thousands of customer support messages instantly by understanding words and phrases that indicate negativity.

Market and competitor research

Another use case of sentiment analysis is market and competitor research. Find out who’s trending among your competitors and how your marketing efforts compare. Get a comprehensive view from the ground, from every aspect of your and your competition’s customer base.

Analyze your competitor’s content to find out what works with the public that you may not have considered. You’ll understand your strengths and weaknesses and how they relate to that of your competitors.

Final Words

Sentiment Analysis is one of those technologies, the usefulness of which wholly depends on the understanding of its capabilities. It can be extremely useful if you know how to use it and it can be completely useless if you apply it on something it is not supposed to do.

Tagx provides sentiment analysis services to a wide range of industries, using our expert workforce’s insights to make each interpretation meaningful. We are experts in interpreting the feelings of a different group of people against various individuals, from social media to other useful online platforms.