The traditional form of data mining is known to all of us. While right now you may be nodding your head sideways, check the example below to understand the data mining format.
| Customer ID | Product ID | Pin Code | Amount |
| 00001 | 10001 | 202020 | 2500 |
| 00002 | 20002 | 101010 | 1000 |
Can you recall something?
We think you can.
Earlier, data miners had structured data which was divided into rows and columns. While data was displayed in the form of rows, columns had information. For example, in the above excel format, rows are the records of customers and the columns define variables relevant to each row. This means that customer with ID 00001 has paid an amount 2500.
This is a highly structured approach which was easy but enough to extract new information which can be utilized by the business.
But, the problem is data is increasing and so is the complexity of data mining. Now, there is a new-age approach known as text mining. The difference is that data mining was achieved with structured data, text mining uses unstructured data. It is not hard to comprehend the increase in complexity from the previous statement.
Hence, in this article, we will discuss how text mining is complex but a useful aspect for the organization. So, keep read to explore more.
Text Mining
Text mining doesn’t use structured data as depicted in the table above. Although text mining more or less performs a similar task, it uses unstructured data which makes all the difference. This is because the unstructured data used in text mining is extracted from digital media, social media channels, surveys, and other online communications. So, the text miner now has two tasks: they have to first formulate the unstructured data and then, draw value out of it.
Below is an example of unstructured data.
The customer 00001 has emailed the company saying, “I like the product A very much. However, I believe that you are deviating from the whole point of providing product A. I am not able to fully utilize the product.”
The customer 00001 has emailed the company saying, “A friend has recommended me your company for a particular product A. After checking your online presence, I was really intrigued and I would like to hear more about the product A.”
The text miner is expected to extract data from this information and find valuable insights from it. How is it possible?
The text miners divide data based on multiple variables. Sometimes, they may only use binary values for variables. Other times, they may use standard deviation for variables.
The process of extracting this information from the sentences makes all the difference in the world.
In the next section, we will discuss this approach.
Text Mining Process
- The first step is to remove the elements of no value from the data. For example, you can remove all the punctuation, prepositions, and pronouns from the sentences. This phase is called data hygiene because after removing these aspects, you can understand if the data is of any value or not.
- In the second step, you should assign the frequency to all the elements. For example, in the above-unstructured data, the product appears 5 times. Similarly, assign a frequency to all the necessary variables or elements of the unstructured data.
- Lastly, try to find relations between these elements or variables. This type of analysis is usually achieved with the k-means algorithm or cluster method.
Sentiment Analysis
Wait, there’s more to the process!
In all the above three phases, nowhere we have discussed the customer sentiment. The whole point of text mining is to understand the customer sentiment behind writing to you or reaching out to you.
For instance, check the two statements given below:
- I love this company. It took me 7 days to reach out to an agent.
- I love this company. They offer great services.
It is not hard for any human being to detect that the first statement is more sarcastic and second is more positive. But, telling it to a machine is rather complex. Now, you may ask then why not allow humans to complete it?
The reason that this analysis is not manually achieved is because it is not easy for human interpreters to sit with thousands of sentences every day. That simply requires more manpower which means more money and more human errors. When machines execute this task, it may be complex to design but it is efficient and error-free.
Conclusion
Text mining is necessary for the corporate world. Every company that interacts with users or receive queries from users can benefit from text mining. This is because text mining can help a business understand the motive and sentiment behind an email or query. Knowing this motive makes it easier for human professionals to resolve the issues at earliest.
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