To run a successful business nowadays, you need to build a strong and popular presence on the Internet. This is best done with the help of social media platforms and with a website. However, building a website is not enough to keep customers happy. You will need to do a lot more. One of the more important things you will have to do is machine learning text analysis or simply referred to as text mining.
What exactly do these terms mean and why are they useful for businesses? Well, it is a useful piece of technology that depends on machine learning (what most people know as artificial intelligence even though it is not exactly AI) to analyze, research, and get an insight into a certain piece of textual information.
Considering that it is impossible for humans to go through millions of comments from different users, emails, articles, reviews, or whatever it is, we have to resort to machine learning to automate the process. Computers can go through information millions of times faster than our minds. This is why we have to utilize that power.
The interesting thing about machine learning is that technologies and trends are quickly replacing themselves. Since we are still “newbies” in artificial intelligence, we learn new things about it every day. Because of that, I wanted to talk about those latest technologies and trends that are related to machine learning and text analysis.
Searching for word frequency
This is not exactly a new technology, it is just a technique that is constantly being improved to be faster, more efficient.
Understanding how these techniques work is not complicated at all. Even though it is a very complicated technique. There machine learning, the most frequently used words in a piece of textual information is then turned into numbers and then into statistics or grass. This makes it easy for humans to understand the data in front of them.
You can utilize this word frequency technique to find out all kinds of things. Whether it is to analyze thousands of reviews of your products and services or whether it is comments on your business’s social media profiles, etc.
Once you read through the data, you will know exactly what your customers are asking or looking for without individually reading through their messages.
Of course, this could also be useful for articles, emails, or even YouTube video titles. It could be used for anything you can think of.
Focus on co-occurring words – Collocation
Collocation is actually quite similar to how word frequency works, but with one extra step. The “AI” is going to be looking for two words that are frequently put together, one after another. Or more accurately put, words that co-occur.
There are hundreds of examples for these words that co-occur in sentences. Bread and butter is a great example. You can very commonly find these words together in sentences. With machine learning and the collocation technique, this can be easily detected.
Usually, the collocation technique depends on bigrams (two words) or trigrams (three). Anything above that will bring errors in the data.
How is this useful? Well, for machine learning to be always accurate, it requires a lot of power. However, with certain techniques and methods, you can improve accuracy while reducing the need for processing power.
Of course, collocation analysis is very complicated and there is a lot more to it than my simple explanation. You could always discover more about ML & collocation analysis on the site: serokell.io.
I do not think that many would explain collocation to be similar to collocation in any way, but to make things a little bit easier to understand, I am going to say they are a bit similar.
With concordance, you can get a lot more context while processing the textual information by identifying and focusing on certain words in a sentence. However, as you probably already know, one word or several words can have several different meanings.
To get accurate data from machine learning, context is required for the sentence. So, the focus is on that one word, but we are also looking for the preceding and the following context. After that, the information can be properly processed and we can get relatable data that can make sense.
This is a much more advanced technique that allows systems to try and understand the “meaning” or the sentiment of the text. This is also very commonly referred to as text classification. It basically allows the computer to categorize the information. The category or the tag will depend on the opinion of the person writing the comment, email, review, or whatever it is.
Here is an example. I am an angry customer that is not satisfied with the product and I decide to leave a review. In my review, I write a sentence that commonly mentions things such as “bad”, “broken”, “cheap”, expensive and you get the point. I am writing sentences to convey that I have had a negative experience with the product.
Through opinion mining, computers can understand whether the message is negative, positive, neutral, or some other category. So, instead of processing the text itself, the computer is trying to put the emotions of the users into readable data.
I know, it sounds impossible, but this does actually exist, it works and it is very commonly used by a lot of companies to process ratings and reviews. You can imagine how difficult it would be for companies such as Amazon if they did not have access to this kind of technology. Without opinion mining or even basic machine learning, processing millions of reviews would be impossible.
There are probably so many different complicated technologies, techniques, and methods in machine learning and text analysis, but I think that the ones I mentioned above are the most popular and most commonly used in every industry nowadays.
I hope that this article was pretty straightforward and informative enough to teach you a little bit about artificial intelligence and what we can do with the power of machine learning.