With the development of artificial intelligence and its pervasiveness in various fields, we are witnessing the supply of artificial intelligence organs in daily life. One of the functions of artificial intelligence in text mining is to analyze emotions with artificial intelligence.
What is emotion analysis with artificial intelligence?
If we take customer feedback as an example, emotion analysis (a type of textual analysis) measures a customer’s attitude toward aspects of a service or product. Emotion analysis usually involves receiving a text, whether a sentence, a comment, or a complete document. Finally, a score is assigned as output to that text, indicating how positive or negative that view is.
Consider positive or negative feedback, for example:
- Positive Feedback: It was a wonderful cafe. The staff was very friendly, and their coffee tasted good!
- Negative Feedback: I do not recommend this coffee shop to anyone; their coffees are awful and expensive!
Why do we need to analyze emotions with artificial intelligence?
In today’s environment where we suffer from data overload (although this does not mean better or deeper insights), companies may have a mountain of customer feedback. However, it is impossible for humans to manually analyze this volume of feedback without any error or bias. Too often, companies with the best intentions and attitudes find their product without feedback. You know you need customer insights to make better decisions about where to go, and of course, you know that sometimes you lack those insights. But you do not know how to access them.
Using artificial intelligence to analyze emotions provides you with answers to the most important issues. Because you can automate emotional analysis, you can make decisions based on large amounts of data rather than simple intuition, which is not always true.
It is impossible to analyze large amounts of data without error.
Imagine this scenario: You own a small shipping business and receive about 20 responses to your email surveys a month. You can (and should) read these items yourself and do your analysis by hand. Now imagine receiving 30,000 responses a month. That’s over a thousand answers every day! Doing this as part of the business owner’s daily routine is impossible.
Read: Introducing a variety of machine learning methods!
In addition, there is the issue of bias. We are all familiar with the days when things go wrong for us, and we even have a bad mood before we get to the office. The risk is that you will negatively interpret messages and any kind of communication. You may also have your own personal and pre-perceived opinions on the subject. This can affect how you interpret the text you want to analyze. You should also summarize the feedback into a few actionable perspectives so that using it makes sense for your company. Finally, views must be translated in a presentable way to be easy to understand. The
Therefore, emotion analysis is important because companies want their brand to be able to evaluate positively or at least more positively than competitors’ brands.
What is artificial intelligence analysis useful for?
The application of artificial intelligence emotion analysis is to quickly obtain a good view of large volumes of textual data. Emotional analysis with artificial intelligence, in addition to analyzing customer feedback, has two other uses:
An example of this is stock trading companies, which make buying or selling opportunities based on the news they receive from the Internet. Here, emotion analysis algorithms can identify companies with positive emotions about them in news articles. So, being aware of this news can mean a tremendous financial opportunity, as it may motivate people to buy more shares of a company. Access to this type of data allows traders to decide the stock before the market reacts.
The following is an example of how a company’s stock price can have affection on the news—emotions expressed in the news trigger a stock trading algorithm to buy stocks before the price rises.
Another application of emotion analysis is monitoring and measuring emotions for social media posts. During the Brexit announcement, a social media emotion analysis tool predicted that those who would vote for Britain to stay in the EU would probably, just like six hours ago. The announcement of the final results is in the minority. (Image from DMNews )
Improve the customer experience
Emotion analysis can examine word-of-mouth customer feedback that the emotions behind them are highly negative. Likewise, we can examine positive customer feedback to understand why these customers had satisfaction with the product or service. After considering the customers’ opinions and being informed of the positive and negative points, better decisions can be made about promoting the product or service.
Read Applications of machine learning and artificial intelligence on YouTube.
When emotion analysis is useable with artificial intelligence with subject analysis, we can limit the information to understand exactly what topics are being about with positive or negative emotions.
Two basic methods for analyzing emotions with artificial intelligence
- Rule-based sentiment analysis
The first technique is based on rules and uses a dictionary labeled emotion to identify the emotions of a sentence. Emotional scores usually need to be combined with additional rules to reduce the scores of sentences that include negation, sarcasm, or related phrases. See the table below for an example.
Sentiment Word
0,5 good
0.8 great
0.8 terrible
۰.1 alright
A positive sign means a positive score, and a negative sign is a negative score.
۲. Machine Learning-based sentiment analysis
Here, we teach a machine learning model for recognizing emotions based on words and order and using an emotionally labeled training set. This approach largely depends on the type of algorithm and the quality of the data used for training.
Let’s look again at the stock trading example mentioned above. We consider headlines, limiting them to the lines of the particular company we are interested in (often by another NLP technique, called Named Entity Recognition) and then orienting the emotions behind the text. We measure.
One way to adapt this approach to other issues is to measure emotion from other dimensions. You can pay attention to certain emotions. For example, consider how angry he was when writing the text. Or how much fear is conveyed in the text?
What are the benefits of using AI analysis with emotions?
Using emotion analysis, you measure how customers feel about different areas of your business without having to read thousands of customer reviews at once. If you have thousands of feedbacks a month, one person can’t read all of these answers. By using emotional analysis and automating this process, you can easily get into the various customer-related areas of your business and better understand how customers feel in those areas.
Disadvantages of using emotional analysis
While emotional analysis is useful, it is still not a complete alternative to reading customer reviews and surveys. Sometimes, there are subtle, subtle, and useful points in the comments. It can be said that if emotional analysis can help you decide exactly which ideas to read, the nuances hidden from the AI will also be considered by you. The
How does emotion analysis work with artificial intelligence? The
The traditional way of analyzing emotions is to use reference dictionaries to determine the score of words and then calculate the average of these scores as the emotional score of a text. The
A simple modeling machine model is then used for classification. This is done by recognizing some features from the text and then predicting a tag. An example of creating a feature is dividing the text into words and then using these words and their frequencies in the text as a feature. The
Use machine learning to evaluate what positive or negative words look like
A “label” measures how positive or negative emotions are. Once the problem is identified, mathematical optimization techniques are used to create a model. The key difference between machine learning is that it determines how positive or negative they are, rather than checking the information in words or attributes in dictionaries. The
Traditional machine learning techniques can produce logical results, but they also have problems such as manual work in creating features. They also do not have a good solution for word order. These problems have been there by a family of machine learning techniques known as “deep learning.”
Different types of deep learning techniques
Deep learning techniques are also famous as artificial neural networks. These techniques have made great strides in natural language processing in recent years. The
In recent years, a special LSTM or Long Short-Term Memory model has been present in most NLP-related cases to achieve the most advanced results. The LSTM method reads the text logically and stores information relatable to what is going on. The
In LSTM, some cells control the information and the information that is not there. In the field of emotional analysis, negation is very important. For example, the difference between “Great” and “Not great” is significant. An LSTM trained in emotion prediction learns that negation is important and works well in understanding what words should be rejected. LSTM can learn grammar rules by reading many texts. The
Deep learning structures continue to evolve through innovations such as the sensory nervous system, which is an unsupervised system (a system that does not require marked training data) developed by OpenAI. Google has also developed Transformer, and more recently “pre-training” (pre-training is where you train a model in a different task.
before fine-tuning your needs with your specialized data set) with a BERT technique added that it has also achieved extraordinary results.
An example of emotion analysis by open.ai
How to use emotional analysis to assess customer feedback?
What do you do with emotional analysis scores? The simplest thing is to measure the intensity of the emotions among each of your responses and use the average to measure the overall emotion about your service or product. From here, you can look at data segmentation and compare different sections. For example, if your business operates in different locations or has demographic information, you can use it to segment customers.