Text Mining and its Applications

For businesses, the large amount of data generated every day represents both an opportunity and a challenge. On the one side, data helps companies get smart insights on people’s opinions about a product or service. Think about all the potential ideas that you could get from analyzing emails, product reviews, social media posts, customer feedback, support tickets, etc.

Data can be internal source (interactions through chats, emails, surveys, spreadsheets, databases, etc) or external (information from social media, review sites, news outlets, and any other websites).  On the other side, there’s the dilemma of how to process all this data. And that’s where text mining plays a major role.

If you need to examine tons of reviews in ”The Times of India’ or in a ‘review site” to understand what customers are praising or criticizing about your brand. A text mining algorithm can help you identify :

• the most popular topics that arise in customer comments, and
• the way that people feel about them: are the comments positive, negative or neutral?
• You can also find out the main keywords mentioned by customers regarding a given topic.

So, what is Text mining?

Text mining is the process of transforming unstructured text data into meaningful and actionable information. It utilizes different AI technologies to automatically process data and generate valuable insights, enabling companies to make data-driven decisions. It involves extracting information from different written resources – like websites, books, emails, reviews, and articles. High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning.

Methods for Text Mining
It includes basic as well as advanced methods as mentioned below:

• BASIC METHODS
• Collocation
• Concordance
• ADVANCED METHODS
• Text Classification
• Text Extraction

Basic Methods of Text Mining

Word frequency, Collocation and Concordance are the three basic methods of text mining.

Word frequency can be used to identify the most recurrent terms or concepts in a set of data.

Finding out the most mentioned words in unstructured text can be particularly useful when analyzing customer reviews, social media conversations or customer feedback.

For example: Expensive, overpricing, overcharging, high pricing

Collocation refers to a sequence of words that commonly appear near each other.

The most common types of collocations are bigrams (a pair of words that are likely to go together, like get started, save time or decision making) and trigrams (a combination of three words, like within walking distance or keep in touch).

Identifying collocations — and counting them as one single word — improves the granularity of the text, allows a better understanding of its semantic structure and, in the end, leads to more accurate text mining results.

Concordance is used to recognize the particular context or instance in which a word or set of words appears. We all know that the human language can be ambiguous: the same word can be used in many different contexts. Analyzing the concordance of a word can help understand its exact meaning based on context. For example – It is expensive for me but save much of my time in office work.

Advanced Methods of Text Mining

Text classification and Text extraction are the two advanced methods of text mining as mentioned below.

Text Classification is the process of assigning categories (tags) to unstructured text data. This essential task of Natural Language Processing (NLP) makes it easy to organize and structure complex text, turning it into meaningful data. With text classification, businesses can analyze all sorts of information, from emails to support tickets, and obtain valuable insights in a fast and cost-effective way.

Some of the most popular tasks of text classification –

• Topic analysis – helps you understand the main themes or subjects of a text, and is one of the main ways of organizing text data.
• Sentiment analysis – helps in analyzing the emotions that underlie any given text. It helps you understand the opinion and feelings in a text, and classify them as positive, negative or neutral.
• Language detection – allows you to classify a text based on its language. One of its most useful applications is automatically routing support tickets to the right geographically located team. Automating this task is quite simple and helps teams save valuable time.
• Intent detection –helps you to recognize the intentions or the purpose behind a text automatically. This can be particularly useful when analyzing customer conversations.

For example, you could sift through different outbound sales email responses and identify the prospects which are interested in your product from the ones that are not.

Text Extraction is an advanced text analysis technique that extracts specific pieces of data from a text, like keywords, entity names, addresses, emails, etc.

By using text extraction, companies can avoid all the hassle of sorting through their data manually to pull out key information.

The key tasks of text extraction –

• Keyword Extraction: keywords are the most relevant terms within a text and can be used to summarize its content. Utilizing a keyword extractor allows you to index data to be searched, summarize the content of a text or create tag clouds, among other things.
• Named Entity Recognition: allows you to identify and extract the names of companies, organizations or persons from a text.

Feature Extraction: helps identify specific characteristics of a product or service in a set of data. For example, if you are analyzing product descriptions, you could easily extract features like color, brand, model etc.

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