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Text Mining:
Unravel the Potential of Business Data

February 18, 2016

Crucial business data is an important asset for every business establishment. Businesses have to deal with both unstructured and semi-structured data sets that are getting generated at the speed of thought. Sources of these data sets might range from social media, application forms, call center records, streaming data from emails, as well as electronic text.

This unstructured data might encompass critical information about customers, business competitors as well as partners. Enjoying access to these highly significant data sets is not enough. Rather, there is a need for tapping into this crucial data to gain better knowledge about customer demands, strengthen business partnerships, as well as achieve a competitive edge in the market. Performing profound analysis of these data sets can prove to be highly beneficial for businesses. Proper data analytics leads to effective insights for strategic moves and better business decisions. In the world of incessant chatter on the web, text mining has emerged as an important field of digital analytics.

Understanding text mining

To put it in simple words, text mining or analytics is an effective manner of transforming unstructured data into useful business information. Unstructured data happens to be qualitative in nature and encompasses several aspects such as data appearance, text color, as well as the textual description. According to the definition mentioned in The Oxford English Dictionary, text mining is the practice or process of scrutinizing written sources for the purpose of information generation. And this data scrutinizing and information generation is done with the help of specialized software programs.

How does it work?

  • Text sources

    The availability of the text is of paramount significance. Data sources can include both external and internal sources. Corporate documents, research papers, social media chatter, service notes, application forms, and emails are some of the most popular sources.

  • Preprocessing techniques

    As the next significant step, there is a need for identifying the data for analysis and mining. By utilizing perfect preprocessing techniques, unstructured data is transformed into usable, structured data format. Some of the important preprocessing techniques are as follows:

  • Text cleansing
  • Tokenization
  • Syntactical parsing
  • POS tagging
  • Information extraction

Applications of text mining

With this prologue on the definition, importance and functioning of text mining or analysis, we move on to its various applications. Some of the major avenues of text mining applications are as under:

Applications of Text Mining

With this prologue on the definition, importance and functioning of text mining or analysis, we move on to its various applications. Some of the major avenues of text mining applications are as under:

  • Content Analysis

    One of the significant avenues of text mining is content analysis. The process involves the qualitative analysis of a content based on readability, reader preferences, mood, and gender bias. When the amount of content is huge, such of text analytics can point out several essential facts about the quality and the tonality of the content.

  • Information Extraction

    Information extraction helps in the identification of key phrases and their relationship within textual data. The process utilized in information extraction is known as pattern matching. This process identifies the pre-defined sequences in text materials and aids in the process of extracting critical business information. Information extraction can prove to be highly beneficial while handling large volumes of data. The information thus collected will find a place in data repositories, and can prove helpful for knowledge discovery.

  • Sentiment Analysis

    Also known as opinion mining, this particular process helps in the extraction of both positive and negative opinions expressed by your target customer group. You develop an idea on customer opinions, choices, and preferences on the digital media. Especially, sentiment analysis in social media platforms will help you get a clear idea of your customers’ thought processes. Knowing about your customer sentiments will prove to be highly beneficial while designing marketing and customer support strategies for your products and services.

  • Topic Tracking

    By keeping track as well as record of user’s preferred texts and documents, topic tracking helps in the identification of similar documents, thus generating consumer interest. The concept of topic tracking plays a key role across industry verticals. With the help of these systems, business owners can collect complete information on services and products offered by their competitors. As the most desired result, they constantly stay updated on latest market trends, news, as well as new arrivals in the market.

  • Optimizing search results

    Text mining and data analytics also find application in optimizing search results. Based on the type of text phrases used together for searching for a specific product or services, the results can be made more relevant.

  • Information Visualization

    There is no denying the significance of a strong visual impact. Text analytics systems utilize visualization tools to present information in a better fashion. Graphical representations are used to express complex business data and information. For instance, building text clouds based on the propensity of various words/phrases used. By mapping text on the geographical map, it becomes easier to see the connections and generate better insights.

  • Automated answering of Questions

    Effective customer interaction is of paramount significance for all business owners and entrepreneurs. Text mining helps in automating the question-answer process, thus resulting in better communication with potential clients. With the help of this application, your live chat sessions will get executed smoothly.

  • Spam Filtering

    Spam filtering is another practical application of text mining. Proper text analytics leads to the perfect identification of spam messages, thus segregating authentic texts from junk.

  • Cross-sell Suggestions

    E-Commerce websites like Amazon, Best Buy or Home Depot, use state of the art text mining algorithms that help the customers find products they are looking for sometimes exceeding their expectations. Consider a consumer searching for a specific running shoe like ‘Air Max 330T’ by Nike, unfortunately that product is out of stock. It would be dumb for returning an Out Of Stock/Product Not Found reply, leaving money on the table where you could have influenced the user with a similar product also called a substitute. Text Mining is one of the aspects that comes handy here helping the search console find a product that matches the product description of the unavailable product mine it and finds similar solutions and presents it to the consumer, preventing a drop-off, even better an engaged converting consumer.

  • Enriching Machine Learning Algorithms

    Apart from the ones mentioned above, text mining also finds effective application in Natural Language Processing or NLP. Human speech/text is analyzed and classified in grammatical units. Computers can perform this particular process through Parts-of-Speech tagging and natural language parsing enabling the same word to be interpreted to different meanings based on the context, thus taking forward the concept of machine learning. Hence empowering an ensemble of other machine learning algorithms to work in tandem and arrive at more accurate outcomes and predictions.

Relevant textual data is of paramount significance for every enterprise. However, the benefits will only be realized, when these business data sets offer profound information on market trends, customers, and business competitors. As an innovative technique of comprehending these data sets, text mining surely deserves its fair share of accolades.


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