Sentiment Analysis: The Success For Brand Reputation Lies In Language
Sometimes it happens that brands need to have a sentiment analysis. Knowing how people talk about your brand is essential. What do your community members think about your company? Do they praise you or do they mock of you? Are they sincerely impressed or that enthusiasm hides a sarcastic and brutal critic? How could you do it?
Sentiment analysis allows you to analyze people’s comments and opinions about you and use those data to fix your strategy eventually.
However, what happens if your community has thousands of people? Indeed, you can read one by one and take notes. It would be hard to check all their opinion by hands with the necessary accuracy. By the way, you are going to save tons of time (and money) if you use a tool to have the job done.
In this article, we’ll explain why AI-based Sentiment Analysis is the key to the success of your brand reputation – and how to achieve it!
- Sentiment Analysis: How Context Influences Language
- AI, NLP And Sentiment Analysis: How To Use It And A Few Examples
Sentiment Analysis: How Context Influences Language
Language is a fascinating and tricky element of our world. We already said that spoken and written communication is a capacity that only humans developed, and we can use it in different ways. We can create art with words, but we can also build exciting worlds and contexts. A simple sentence can conceal many different meanings.
Let’s say, for example, that you are reading reviews of the products you’re selling on Amazon. You find out a zero star rated review that explains why the user didn’t like it. The report may end in this way: -It worked severely since the first moment. Congrats! That’s precisely the great tool I need, to be a happy crazy guy.-
You and the other customers will understand that the user is sarcastic and that he meant the opposite of what he said. It is clear that happens you know the context of the message, and you can spot a sarcastic tone of voice.
In essence, it is the process of determining the emotional tone behind words. It helps companies to understand opinions and emotions expressed by their audience, for example, within an online mention on socials channels, on a review, or any other direct communication channel.
It’s a technique tool that marketers know very well. They use it for many years, since the beginning of the web. Once, the sentiment analysis had to be done by hand. You could do it by spotting keywords, selecting the essential words for your brand, and searching for them in an online conversation. Next, to it, there was the classical affinity technique, in which every keyword was labeled with positive or negative sentiment.
This process has many applications because it allows you to know what your clients think about your brand and your products and what they’re saying about you. In case of negative feeling, this helps you to understand how to change the situation.
However, this can work for human beings.
Words such as “great” and “wonderful” have a definite meaning if considered by themselves, even if your intent is sarcastic. If someone doesn’t catch your intentions, it may create a misunderstanding. How can software understand irony and sarcasm?
That’s what most modern sentiment analysis systems based on Artificial Intelligence come to help.
Nowadays, Sentiment analysis, also known as Opinion Mining, has become a sub-field of Natural Language Processing (NLP). Here it comes AI to help you analyze faster and with more accuracy your customers’ opinions.
The algorithm analyzes different elements of the expressions, such as the subject of the conversation or the person who’s expressing the concept.
This kind of analysis can be applied to a full document, a single sentence, or even to a sub-sentence level, meaning it analyzes only some expression of a given phrase. If you’re doing this kind of analysis, you can select the amount of precision you’d like to obtain.
The job you are used to doing by hand is now autonomous.
After analyzing the sentences, the system collects the data, and it classifies them into structured pieces of information. Those bits are usable to your marketing strategy because they contain information about your brand and your products. All of this sounds full of exciting potential, but how exactly is it done, and how can a tool understand the exact meaning of a sentence to be sure not to misinterpret it?
AI, NLP And Sentiment Analysis: How To Use It And A Few Examples
We’ve made clear that Sentiment Analysis can be a great help to monitor what people think about you. Natural Language Processing (NLP), the technique that treats natural language, is nowadays boosted by AI algorithms.
Artificial Intelligence algorithms handle a large amount of data. They extract coherent patterns and analyze the tone of voice and other elements to make possible doing Natural Language Processing at a scale.
There is even more! Coupling Sentiment Analysis with NLP and AI, you can focus on emotions and be more proactive towards your customers. That’s what makes all the difference and allows you to understand if that comment full of pleasant words is a real compliment or a sarcastic critic.
AI can tag parts of speech and learn to read as a human being. As you may guess, having a machine that helps you in your most boring tasks helps you to save time and focus on making your customers happy.
Let’s see some examples of how your brand could take advantage of this technology.
- Feedback analysis. As we’ve seen, this Sentiment Analysis can be useful to understand what your customers think about you and your products and services. You can select data from everywhere, like a forum, social media, and Amazon. In this way, you can see which of your products are well-loved and which ones are improvable. You could even find that your customer has problems that you’ve never even imagined.
- Reputation management. Your products aren’t the only element that can take advantage of accurate sentiment analysis. Your reputation as a brand can discover a gold mine from this kind of research. If you periodically look for what people think about you, the advantages are enormous. You could identify a latent demand for a product that your public crave. Oppositely you could adjust your reputation, answer to eventual critics, and build a stronger bond with your customers.
- Prevent a crisis. Unfortunately, sometimes, a disaster could happen.
However, if you take care of your community’s opinion with accurate monitoring, you could spot when something starts going bad. For example, whereas a product doesn’t work, a business choice didn’t meet the favour of your customers. In this case, you could prevent the bomb from exploding by taking some precautions.
Sentiment analysis is a great chance to understand every conversation about your brand better, and AI can help you make it easier and faster.
Coupled with Social Monitoring activities, you can dramatically improve your brand reputation with minimal efforts.
Have you ever used it for your company?
- Sentiment Analysis is the process of determining the emotional tone behind words. It helps companies to understand opinions and emotions expressed by their audience.
- AI helps to analyze faster and with more accuracy customers’ opinions. The algorithm analyzes different elements of the expressions, such as the subject of the conversation or the person who’s expressing the concept.
- Coupling Sentiment Analysis with NLP and AI, you can focus on emotions and be more proactive towards your customers.
- 12 NLP Examples: How Natural Language Processing is Used
- Sentiment Analysis. Nearly Everything You Need to Know
- Natural Language Processing for Sentiment Analysis
- Sentiment Analysis: Thanks to Artificial Intelligence, it’s no Longer Just a Dream
- Sentiment Analysis is difficult, but AI may have an answer.
- What is Sentiment Analysis and How to Do It Yourself