Unstructured Text in Data Mining: Unlocking Insights in Document Processing

We are collecting data like never before, and by 2025, around 80% of this data will be unstructured. Data mining helps shape this data, and businesses must invest in unstructured text analysis to gain insider knowledge about their performance, customers, market trends, etc.

Unstructured data is the unorganized and scattered pieces of information available to a business but which cannot be used by a program or understood by humans easily. This data is defined by a data model, and nor does it conform to any predefined structure. Data mining allows us to sort and process large data sets to find patterns that help businesses get answers and solve problems.

Challenges in Unstructured Text Analysis

Data is collected in different forms and sources, including emails, social media, user-generated content, forums, articles, news, and whatnot. Given the large quantum of data, businesses will likely ignore processing it due to time constraints and budget challenges. Here are some key data mining challenges of unstructured data:

• Nature of Data

  Since there is no definite structure, knowing the nature of data is a big challenge. This makes finding insights even more difficult and complex, which becomes a big deterrent for the business to start processing as they don’t have a direction to follow.

• System and Technological Requirements

  Unstructured data cannot be analyzed with the existing systems, databases, and tools. Hence, businesses need high-capacity and specially designed systems to extract, locate, and analyze unstructured data.

• Natural Language Processing (NLP)

  Text analysis of unstructured data requires NLP techniques, like sentiment analysis, topic modeling, and Named Entity Recognition (NER). These systems require technical expertise and advanced machinery for large data sets.

Preprocessing Techniques in Data Mining

Data preprocessing includes cleaning, transforming, and integrating data before it is sent for analysis. Using the following techniques, analysts improve data quality for easy data mining.

• Text Cleaning

  Text cleaning is about removing irrelevant data from the data sets. It includes removing HTML tags, special characters, numbers, punctuation marks, and other aspects of text. The purpose is to normalize the text data, remove stop words, and remove any element that can inhibit the analysis process.

• Tokenization

  When building the data mining pipeline, data tokenization is required to break down the unstructured data as it impacts the rest of the process. Tokenizing unstructured data includes creating smaller and similar units of data, leading to effective representation.

• Part-of-Speech Tagging

  Part-of-Speech tagging includes labeling every token into a noun, adjective, verb, adverb, conjunction, etc. This helps create a grammatically correct data structure, which is crucial for a wide range of NLP functions.

• Named Entity Recognition (NER)

  The NER process includes tagging entities in the unstructured data with definite roles and categories. Categories include people, organizations, and locations, among others. This helps build a knowledge base for the next step, especially when NLP comes into action.

Text Mining Process Overview

Text mining involves step-by-step task execution to uncover actionable information from unstructured text and data. Within this process, we use artificial intelligence, machine learning, and NLP to extract useful information.

• Pre-processing: Text pro-processing includes a series of different tasks, including text cleanup (removing unnecessary information), tokenization (dividing the text into smaller chunks), filtering (removing irrelevant information), stemming (identifying the basic form of the words), and lemmatization (reorganizing the word to its original linguistic form).
• Feature Selection: Feature selection involves extracting the most relevant features from a dataset. Particularly used in machine learning, this step also includes data classification, regression, and clustering.
• Text Transformation: Using either of the two models, Bag of Words or Vector Space Model with feature selection, to generate features (identification) of similarity in the data set.
• Data Mining: Ultimately, with the help of different applicable techniques and approaches, data is mined, which is then utilized for further analysis.

With the data mined, businesses can train AI models with the help of OCR processing. As a result, they can deploy authentic intelligence to gain precise insights.

Key Applications of Text Mining

Conclusion

Data mining from unstructured text will become a fundamental practice as we progress into a data-intensive world. Businesses will want to discover new trends and insights to build better products and improve customer experiences. Where the operational and cost challenges are most prominent today, they can be subdued with large-scale implementation of data mining techniques. Shaip has expertise in data collection, extraction, and annotation, helping businesses better understand their customers, markets, and products. We help businesses improve their OCR data extraction and collection with pre-trained AI models delivering impressive digitization. Get in touch with us to know how we can help you process and declutter unstructured data.