If you could hear your visitors’ voices when they land on your sites, how does that sound to you? Do you truly want to understand how they perceive your brands? Are you seeking to learn their opinions and feelings towards your services?

Sentiment analysis is all about studying your customer’s sentiments and use them as a powerful tool empowered by AI services to improve interactions. There are several ways to keep track of your customers, but researchers came up with this stunning innovation of analyzing customer sentiment with Artificial Intelligence.

You can study what your customers have to say about your products, learn their sentiments that comprehend their emotional tone in reviews, and gain a rapid first-hand insight no matter how many sets of data is there to analyze. There are sentiment analysis tools available now, many thanks to Artificial Intelligence.

So, stay tuned to gather more knowledge about sentiment analysts and how to apply the concept to your businesses to get started with the examination.

Emotion AI

For example, you bought Skullcandy headphones. You were highly satisfied with the quality of the product but you still felt the urge to leave a review on their site. And your review was ‘Highly-satisfied with my new Skullcandy headphones, the sound quality was just amazing. But it was a bit overpriced though.” For Skullcandy, the sentiments toward the headphones are both optimistic and pessimistic.

AI Sentiment Analysis is most widely known as Emotion AI. It is a mechanical procedure; there are certain algorithms designed that classify statements as positive, negative, and neutral through machine learning and text analytics.

AI was never associated with the term ‘emotion or sentiment’ because people generally found it to be unrealistic. These days, an entire field of ML and AI is focusing on adding some emotions to this tech. This is how emotional AI came into being, and you can take full advantage of this innovation to learn customers’ responses to your products and services.

But how analyzing customer sentiment with Artificial Intelligence works?

The technology revolves around the use of NPL (Natural Language Processing or Linguistic Algorithms) to evaluate good, bad, or neutral opinions. NPL technology is used for sentiment analysis. The language alters human language into something that a machine can easily recognize.

What is necessary to know is: how to capture the useful information? What are the right phrases to catch for analysis purposes? And how to reflect the analysis into businesses.

Once you find answers to the above questions, you can use algorithms to conduct surveys of social media sites, and track interests by running psychological surveys. There is a picture software that can scan reviews, and ratings to conclude how customer’s sentiments can change with respect to content.

Use Case of AI Sentiment Analysis

Social Media Emotion Analysis

Every second, around 6,000 tweets are tweeted on Twitter and it becomes a sensitive task to stay up to speed. Sentiment analysis makes it possible for you to never miss out on an online comment, and monitor responses 24/7 to bring a change.

Benefits of Sentiment Analysis

Do you want to stay competitive in the field? While you are using social media to share your positive/negative/neutral views, businesses on the other hand are gathering your feedback to improve their services.

Let’s take a look into the values of AI sentiment analysis, and its types further down in this article.

1. Convenient Customer Support
2. Quality Product Production
3. Improved Marketing Strategies
4. Increased Sales Revenue
5. Better Crisis Management

Types of Sentiment Analysis

Emotion AI is divided into the following types:

1- Standard Sentiment Analysis

This kind is used as a proxy to analyze customer emotion, where the concept says to segregate text as per some predefined notion of sentiment. The datasets are usually designed with gold-standard sentiment labels, designated depending on the results of manual annotations.

For example categorizing into positive, negative, and neutral.

2- Fine-Grained Sentiment Analysis

This type of AI sentiment analysis understands people’s opinions to give a more concise outcome. The gathered reviews are ranked to ratings such as 5-star:

• Very positive
• Positive
• Neutral
• Negative
• Very negative

Furthermore, specific terms or phrases are associated with the 5-star categories such as ‘anger’, ‘sadness’ ‘worries,’ ‘liked it,’ ‘did not like it’ types.

3- Emotion Detection

Facial recognization can be used as the best example to explain this concept. So to catch exact facial expressions in ML, facial recognition analysis is used to read sentiments.

4- Aspect-Based Sentiment Analysis

This is used to analyze the sentiment of texts only. It is believed that the results calculated using this technique are more detailed, interesting, and proved to be accurate for the reason that aspect-based analysis looks more closely at the sentiment behind a text.

Here’s a breakdown of what can be extracted through this research analysis:

Sentiments: positive/negative/neutral reviews or about a particular aspect.

Aspects: the thing or topic that is being discussed.

5- Intent Analysis

You can say that intent analysis is a step further to sentiment analysis because it tells you about the customer’s exact sentiment or preferably intention behind the message.

In Essence

With vteams, AI algorithms, you can find optimistic, pessimistic, and unbiased reviews about your business products to protect your reputation. You can find marketing insights alter your offers, and improve your customer services effectively.

So, would you try us?