Text Mining, Analytics, and NLP

-Jayant Meshram, Siddhi Mundada, Akash Nachan, Srushti Naik

Intro

It is said that information is the oil of the 21st century, and analytics is the combustion engine. The expansion of the digital universe is one of the most significant things that mankind has faced.

Reports say that, as of now, less than 1% of the world’s data is analyzed and processed. Stats show that we daily create an average of 2.5 quintillion bytes, of which 90% is unstructured.

source: pcmag.com

We can have data without information but we cannot have information without data. Unstructured data is mainly things like text, tweets, pictures, videos, etc. And, in order to get the information, we need to analyze the unstructured data and extract useful insight from it.

The goal of discovering meaning and purpose from textual data created the industry of text mining and NLP.

But how these two are different from each other? If they are, in what context do they vary, and how it lays the path for the applications and the future of the domains? , we are going to discuss that in this blog.

What is Text mining?

Text data mining can be described as the process of extracting essential data from standard language text. All the data that we generate via text messages, documents, emails, and files are written in common language text. Text mining is primarily used to draw useful insights or patterns from such data.

source: textanalyticsworld.com

Once extracted, this information is converted into a structured form that can be further analyzed, or presented directly using clustered HTML tables, mind maps, charts, etc. To process the text, Text mining traces its use case and uses some applications of other areas, some of which, are shown the in the figure above.

Key-Goal

The Key Goal of text mining is to deal with text quality evaluation. It works with both structured and unstructured data. This type of system does not consider semantic features, but can easily deal with: Information pattern searches and Matching structures identification. It focuses mainly on structure rather than anything else.

Some examples of text mining are:

1. Social Media Monitoring

source: pinterest.com

Text mining social media data can help brands to better understand their customers and their customer's experience of their brand, products, and services. In the case of apps like Twitter, and Facebook, text mining helps sites to identify topics and subtopics, it helps to identify the current trending topic. It allows concerned brands in sentiment and emotion analysis, psychographic profiling, and competitor analysis as well.

2. SEO

Source: https://fatjoe.com/

Texting mining combined with network visualization techniques helps in search engine optimization. Using text mining, we can identify the discrepancy between what the user is searching for and what he is getting as a result. Then we promote content that bridges this gap so that its relevant search results are shown at the top.

3. Cybercrime Prevention

Text mining is making cybercrime prevention easier for enterprise organizations as well as law enforcement by establishing more context around the intelligence they are being fed. This enables them to pinpoint real threats and limit the number of false positives created by keywords taken out of context.

Some of the other major applications of text mining are contextual advertising, spam filtering, etc.

Text Analytics?

Two terms text mining, and text analytics are roughly the same. Mining emphasizes more on the process. Analytics, on the other hand, emphasizes more on the result using advanced machine learning algorithms and natural language processing (NLP) on the mined text data.

NLP

Source: businessinsider.com

To put it simply, NLP is a branch of artificial intelligence that deals with communication. It is a field of artificial intelligence in which computers analyze, understand, and derive meaningful information from human language in an innovative and useful way. This method allows machines to create (natural language generation) and analyze (natural language understanding) the human language.

Key-Goal

The Key goal of NLP is to make interactions with machines simple and convenient for people.

It considers grammatical structures and semantic analysis for processing the data whereas text mining focuses only on structure. Apart from textual data, NLP can deal with speech data.

Some of the applications of NLP are as follow:

1. Search Engines

Every time you google something, you upload data into the search engine. It looks for connected results, and when you click on a link, the system decides everything was done correctly and uses your choice to provide better results in the future.

2. Intelligent Chatbots

Source:siamcomputing.com

NLP chatbots keep track of the information throughout the conversation and learn as they go. To ensure that our NLP-powered chatbot doesn’t go wrong, it is systematically trained by sending feedback to improve its understanding of customer intents using real-world conversation data being generated across channels.

3. Spellcheck Apps

We all have used typing assistant apps like Grammarly. These spellchecking apps have huge databases of words, word combinations, and rules. When we type a word incorrectly, the NLP system suggests a correction, making the write-up error-free and much more professional.

How Text mining and NLP are different from each other?

NLP in text mining?

Text mining is mostly rule-based, meaning a developer tweaks certain rules to be followed while analyzing the document. While this method is useful for giving insight into the data, it is not always a best-case scenario.

Consider a company that sells products online. Now a rule-based method will generally look for certain flags, such as price or size, etc, to identify the intent. Eg, consider a customer has dropped a review as:

“Really love the product since it’s so cheap compared to the alternate options that come at such a high price”.

Now, text mining will look for the word high price and treat this sentence as a complaint about pricing, which in reality was good feedback. So, grammatical structure matters, when it comes to applications where human language is interpreted by the machine.

Some of these applications are:

Sentiment analysis, contextual analysis, Social site monitoring, etc.

Conclusion

To conclude, in this blog we have seen what is text mining, what is text analytics, and what NLP is. We’ve also seen their major applications and discussed when put together for certain use cases, how NLP and text mining can be used to produce more advanced and practical applications.

References:

1. https://www.sentisum.com/library/nlp-and-text-mining
2. D. A. Naik, S. Mythreyan and S. Seema, “Relevance Feature Discovery in Text Mining Using NLP,” 2022 3rd International Conference for Emerging Technology (INCET), 2022, pp. 1–6, doi: 10.1109/INCET54531.2022.9824807.
3. https://noduslabs.com/cases/google-seo-strategies-text-mining/
4. https://www.linguamatics.com/what-text-mining-text-analytics-and-natural-language-processing
5. https://sloboda-studio.com/blog/natural-language-processing-vs-text-mining/
6. Y. Kim, J. Lee, E. -B. Lee and J. -H. Lee, “Application of Natural Language Processing (NLP) and Text-Mining of Big-Data to Engineering-Procurement-Construction (EPC) Bid and Contract Documents,” 2020 6th Conference on Data Science and Machine Learning Applications (CDMA), 2020, pp. 123–128, doi: 10.1109/CDMA47397.2020.00027.
7. https://siamcomputing.com/digital-transformation/chatbot/