How to use NLP for sentiment analysis

NLP sentiment analysis

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If you run a brand, you probably skimmed through comments to understand which ones were good and evil. That’s a form of sentiment analysis. If paired with natural language processing (or NLP for short), it happens to be a powerful asset for brands.

Are you sceptical? In this article, we will see why sentiment analysis and NLP are so good together and some ways in which brands can take advantage of them for a better marketing strategy.

If you missed our previous articles about natural language processing, don’t worry: you can find all of them in our blog!

What is sentiment analysis?

As usual, let’s begin with some definitions of what we will discuss.

What is precisely sentiment analysis?

Wikipedia defines it as “natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.”

We have discussed this topic several times in our blog because it is one of the most essential and valuable tools artificial intelligence brings for marketing and branding purposes.

According to your needs and goals, you can use sentiment analysis for many purposes: you can understand what your audience thinks of your brand, how they perceive you, or even what they think about a specific product or service.

It is a gorgeous tool for reviewing the analysis and selecting and isolating some specific words and phrases that you need to analyze better.

Let’s see why marketing can use it profitably and how NLP – natural language processing – and sentiment analysis are best friends.

How can NLP help sentiment analysis?

Sentiment analysis uses machine learning and natural language processing to analyze bits of text, looking for specific words that indicate polarization of opinion. Even if it is pretty standard, it is also one of the most challenging tasks natural language processing can achieve. 

Data analysis has always been critical to marketing strategies. Still, it is even more crucial when brands can train algorithms to improve their sensibility and analysis. The main way sentiment analysis can do it is by associating any comment or bits of text with a “positive,” “negative,” or “neutral” tag. It usually defines the general tone of the statement, thanks to the analysis of the words the user decided to pick.

In this phase, NLP is handy because of what it can do with the data and the bits of text. In particular, some of the most exciting things it can do are looking for specific words and extracting interesting information from several sources – like websites and databases.

Why is it useful for marketing purposes?

Sentiment analysis allows brands to understand what their customers think about them. It is an asset that marketing managers should not underestimate.

If a brand knows what a customer is saying – and how sweet or harsh the words they are using -the next thing it can do is look for why these words are used and improve the criticized department. 

Marketing focuses on perception, which means that what people think and say about brands matters greatly. In this case, NLP and sentiment analysis are hugely helpful in monitoring and checking what’s going on in the field.

Let’s see together a couple of ways in which you can take advantage of this artificial intelligence combo.

Three ways we can use NLP for sentiment analysis

How can we use sentiment analysis and NLP to improve branding and marketing strategies?

Here are three examples.

Improve market and competitor analysis

As we were saying, knowing what customers think is an excellent weapon for marketers. It gives tons of hints on improving products and services, like customer care. You may even find out unexpected things – like people badly wanting back a discontinued product.

Moreover, it helps to confront your work and reputation with the ones of your competitors. What happens if people love another company’s customer service and despise yours? You can fix it only if you know it – and here is where NLP and sentiment analysis come to the rescue.

Prevent and solve the crisis

A reputation crisis is not something that you want to experience. However, to avoid the worst-case scenario, you can always make efforts to prevent it! 

Suppose you constantly manage what your audience is saying. In that case, you can spot specific words to see how your reputation is going.

On the other hand, if you recently made a mistake and you are in the middle of a crisis, you can see what people are complaining about and try to respond most effectively.

Better understanding your audience

It may seem too obvious, but sometimes the simplest, the better. You can improve your marketing, analyze your field, and solve problems. Still, all the above are impossible if you don’t know who you are talking to.

All the data you collect with NLP and sentiment analysis will help you create a more profound and complete idea of who buys your product, why, and their thoughts. It is a mine of information that you can use diffusely, for instance, by creating some buyer personas.

Conclusions

Sentiment analysis and natural language processing are a great combo. You can monitor competitors and reviews and, most importantly, check how your brand is going.

We recommend that you try it and, if you need a hand, contact us!

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