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Business Insights: How to Organize and Categorize Your Textual Data

Contributing Author

7 min read

Jan 5, 2024

Handling complex data can be a daunting task. For unstructured data, the demand can be higher for several reasons, such as noise in data, changing trends, and the ambiguity of the language. However, you need to understand data models to make informed decisions about your business.

Hence, this article is the solution you need to have a broad understanding of textual data. It details how to classify words for improved understanding. Whether you are a beginner or an occasional user, the content equips you with practical strategies and tools to simplify your text classification journey.

Follow closely.

What is unstructured data?

Unstructured data refers to information that does not align with conventional data models. Hence, storing or managing them in relational databases isn't easy. Today, most newly generated data is unstructured. However, there are tools to manage and analyze unstructured data for business intelligence.

Unstructured data mining can be textual or non-textual. The data always have an internal structure but lack that predetermined data model. Most times, they are generated by either human or machine.

The commonest type of unstructured data is text. Unstructured text is collected in several formats like Word documents, PowerPoint Presentations, email messages, transcripts from call center interactions, survey responses, social media sites, and blog posts. Other types include audio and video files, images, and machine data.

Unstructured data vs. structured data

The major difference between structured and unstructured data is because of its type of analysis, format type, schema used, and storage method. Structured data are categorized, and you can easily search for them. Sometimes, you can use both structured data and unstructured data together.

However, for textual data, business analytics has highlighted how important it is to classify text. You want to uncover hidden patterns, market preferences, customer preferences, market trends, and correlations. Classifying text helps to sort words and make sense of the information. Also, you can easily access your data within a short time.

The advantages of classifying text include the following:

Its scalability: Companies can structure a large amount of information like social media, emails, documents, support tickets, and chats within seconds.
Real-time analysis: Thanks to technology, you can detect information and identify urgent ones. That way, you can take action as soon as your company should.
Consistent results: Humans can make errors. Classifying text provides an opportunity for a second examination. Little wonder, the text analysis industry is known for more accuracy.

Examples of unstructured data

There are several examples of unstructured data. The commonest ones include:

Business documents
Medical records
Social media posts
Videos, images, and audio media content

Examples of semi-structured data

For semi-structured data, the examples include:

Email
Electronic data interchange
NoSQL databases
XML, CVL, and JSON documents

Examples of structured data

Examples of structured data are:

Social security numbers
Customer name and email address
Dates and times
Product prices and serial numbers

How to choose the right group to organize information (and the tools you need)

Text classification is one of the most useful natural language processing techniques due to its ability to structure, organize, and categorize all forms of text to solve problems and deliver meaningful data to solve problems. You can also explore natural language processing (NLP), a machine-learning process that can classify text as humans do. The text classification tasks include sentiment analysis, language detection, topic modeling, and intent detection.

You can select the right groups for words to organize information by the following methods.

Data gathering

If you want to gather data for your company's products and services, you can do it internally or externally. Here is a quick examination of both:

Internal data

This data is generated daily from chats, emails, customer queries, surveys, and customer support tickets. You can directly retrieve your software or platform as Excel or CSV files or via an API.

Customer service software

You use these software to communicate with customers, resolve their support issues, and manage user queries. Common examples include Freshdesk, Help Scout, and Zendesk.

Chat

These apps are for communication with team members and customers. They include Slack, Intercom, Hipchat, and Drift.

CRM

You can monitor interactions with clients and potential clients. They range from customer support to sales. Common examples are Pipedrive, Salesforce, and Hubspot.

Databases

Your database is the home of information. It helps to manage, analyze, and store data. They include MongoDB, Postgres, and MySQL.

External data

The external text data will include data from everywhere on the web. They include news reports, social media, forums, and online reviews. You can use APIs, web scraping tools, and open datasets to gather this data.

Web Scraping Tools

You don't need any coding experience to build your web scraper. You can employ tools like Portia, Dexi.io, and ParseHub. If you are a coder, you can use Wombat in Ruby and Scrapy in Python for the scrapers.

APIs

Most social media platforms have their APIs. You can leverage this to gather customer's comments and reviews or search archives for researchers.

Integrations

There are SaaS tools that can perform text analysis for you. They can carry out complex data mining tasks. The cloud solutions provide ready-to-use services for instant analysis.

Data preparation

This is the organization of your data for easy analysis. You can employ the use of natural language processes like:

Tokenization, parsing, and Part-of-speech techniques.
Dependency parsing.
Lemmatization and stemming.
Constituency parsing.
Stopwords (like a, and, the, or, etc.).

How to read and interpret grouped words for improved decisions

An important question about structured data is how to read and interpret them. There are two techniques – basic and advanced.

Basic techniques

Word Frequency

This process is straightforward. It is about the frequency of phrases or sets of words in a dataset. For proper understanding, you can know the number of relevant keywords and visualize the results via graphs, charts, or a table.

Concordance

Concordance is a great feature that uses the concept of context that is valuable to you. It eliminates the ambiguity of words and text with differing antonymic meanings.

Collocation

This analyzes language constructions like bigrams or trigrams.

Advanced techniques

Text classification

This technique helps to assign content into categories for easy reference. It has several applications, such as topic analysis, sentiment analysis, language detection, and intent detection.

Text extraction

It helps recognize and extract keywords, models, brands, and specs, and create reports easily.

Insights on common challenges small business owners face

The most common challenge small businesses face with text classification is the ambiguity and complexity of the human language. In a document, the same word can be used in different contexts and even different meanings. As such, they should have varying interpretations.

Other common challenges are:

Multilingual text refining
Cost
Usability
Domain knowledge integration

Success stories and real-life use cases

There are several applications of text classification. Outstanding real-life use cases like;

Risk management: Finance organizations can extract information and monitor shifts in sentiments. Businesses can easily study industry trends and gain insights.
Customer service: Improve customer experience with their feedback. You can also facilitate real-time responses and prioritize their issues.
Healthcare: Medical researchers use automated information clustering and extraction for tailored purposes.
Maintenance: Detect patterns to study maintenance processes.

Integrate text classification for your specific needs.

Now is the best time to integrate text classification into your business. You would agree that the real-life use cases above are solid examples of how text classification can fulfil your specific needs. Integrate text classification into your business relational database now and gain all the business insights your brand needs to be ahead of competitors.

Now is the time to get started.

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