How to Understand Machine Learning in Text Analysis?

When most people talk about analytics and big data, things that come into mind are statistics, numbers, spreadsheets, and generally, math. Since data is mainly in the form of numbers, it is understandable. The data the computer understands is only the ones that come in the binary form; Os and 1s.

However, most files comprise larger spectrums of information, ranging from emails to text messages to loan application forms to image data to voice recordings, etc. These different types of data carry ridiculous and outrageous amounts of information. Text data is no different.

According to statistics, by 2022, there will be over three hundred billion emails sent daily. In the past decade, text messages sent have increased by more than seven percent. People of the younger generation are beginning to choose text messages over calls. You can check here for more on mobile messaging. 

These numbers are just scratched on the surface as there are other different types of data in text forms. They include insurance application forms, product descriptions, support tickets, healthcare records, etc.

Extracting something meaningful from different texts is as complicated as it sounds because texts usually have different formats and contexts. Textual data is commonly called unstructured data because there is no precise format for storage or predefined data models. 

Daily Application of Text Analysis

Text analysis applications are far and wide, from basic automation to more advanced interactions between the computing systems and the people operating them. Its complexity usually leads to the formation of its rules.

We can now begin to look into machine learning in text analysis with this bit of knowledge as it applies to data processing. Text analysis can be described as the process of getting valuable information from different texts.

Every piece of information can be perfectly analyzed at deeper levels better to understand the topic or author of the text. Introducing machine language text analysis can help provide users with excellent services like;

  • Answers to frequently asked questions.
  • Translation into various languages.
  • Monitoring how people feel toward specific products and services.
  • Facilitating paperwork through classification and clustering of different documents

How Does One Build Text Analysis tools?

Machine language can work perfectly with various types of textual info like media posts, emails, and messages. Most of the time, special software can be used to analyze and process the information.

Gather All the Data

First, you need to decide the information you wish to study and how you would want to collect them. The samples of this numbers can be used to test the models. Internal statistics is generated by every company or person daily, and there is external data, which can be gotten from newspapers, the internet, and forums.

Prepare All the Data

If there is unstructured info, they need to be preprocessed and prepared. Without this, the program would likely not understand it.

Use a Machine Learning Algorithm for Text Analysis

It is possible to write algorithms from scratch or simply use a library. There are a lot of resources online which can be used for research and study.

Well, some techniques can be used for machine learning in text analysis. They are listed and discussed below;


Every available token should have a meaningful part or unit. Punctuation and words are classified as tokens, but white spaces are not.

Part-of-speech branding

When a grammatical section or category is assigned to different tokens, it is known as part-of-speech tagging.


When affixes (prefixes and suffixes) are eliminated from words, the stem is gotten. Google as a search engine uses stemming to index requests. Instead of having to store all the word forms, lexicons are usually cut down to stems. This makes the process a lot faster but a little less accurate than the process of lemmatization.


Setting a word back to its main dictionary form is helpful for language processing. You can always map all the forms of an expression to a ‘root’ verb, and it would still be easy for the machine to understand it.

What Are Some Practical Applications of Machine Learning in Text Analysis?

There are many practical application of machine learning and some of the common ways this procedure is used in programing includes 

Natural Language Processing

NLP helps the machines understand the language of humans and act based on those requests. These systems can be used as smart assistants, in voice recognition systems, and for chatbots.

Monitoring Social Media

Do you love your brand? If yes, you need to know how clients perceive the goods and services you offer. ML in text analysis can help you do just that. This link has more on how make good use of your social media stats. 

Offering Customer Service

Entrusting ML with routine work helps employees focus more on areas requiring more human attention. You can use these techniques to identify problems, ticket tagging, and assigning tasks to people. The system can even help you prioritize any requests.

Building Business Intelligence

ML is excellent for a better understanding of statistics and trends by helping you visualize and analyze all types of external and internal data. It can also be used in marketing and sales, SEO, robotics, etc.

Take Away

There may be some challenges associated with Machine learning in text analysis. They are complex, understanding the culture of humans and conceptual struggles. However, in general, this technology is widely applicable in different areas for an all-around improvement.

Fry Electronics Team

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