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Text Analytics: The Key to Making Informed Business Decisions

Irrespective of whether data is qualitative or quantitative, one can use several methods and many tools for data extraction and analysis. What if one needs to extract the most important or stressed topic or the most repeated word from within millions of statements? Obliviously, one can’t sit and read the whole text. Hence, in order to overcome all these difficulties, text analytics was introduced in the mid 1980s.

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Text analytics or text mining is the most important part of advanced analytics. Text analytics refers to extracting high quality information from huge text data sets. Text analytics forms a crucial part of any organization’s core processes. This is because one must first understand the cultures of customers and stakeholders in order to design better products, systems and services. And this can only be achieved by identifying the linguistic patterns and trends of customers and stakeholders.

One may opt for primary research or research articles to collect the opinions of stakeholders. However, both methods culminate in a huge set of scattered data. As a result, it is difficult to consolidate such data for analysis. Under such circumstances, text analysis comes to the rescue.
Text analysis can be conducted using a wide variety of tools. IBM SPSS, SQL, SAS and Rattle are a few of the leading text analysis tools.

If we use Rattle as a tool for text analysis, several steps have to be followed. For instance, all the punctuations, special characters, helping verbs, conjunction, paragraphs and white space have to be removed as they may affect the end result. After obtaining raw data,one may summarize the most repeated or stressed word or words that are associated with the repeated ones. For example, consider some product opinion posts in a social network. The numerous comments on a particular post can be considered as the text data set. By identifying the most repeated word, one can easily identify whether opinions are tending towards positive or negative. Further, by looking at words associated with the opinions, one can easily make out what they are talking about. One can also use a word cloud for this.

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This word cloud is a pictorial representation of data that has been retrieved after analysis of a huge volume of back-end data. In a word cloud, words demonstrated in bigger sizes are the ones that frequently appear in a given text. Looking at this collaboration of words, one can easily make out the most stressed on topics of the article. For instance, in the word cloud illustrated above, the most repeated words are tax, people, system, benefits, need, work etc. So, we can say that the article referred to in the above example is about tax system and its benefits, their relationship with work, people income etc. It’s basically nothing but a summary of the whole scenario in a concise single picture.

Talking about the uses of text analytics and the word cloud, they are important analytical tools that are used across a majority of firms. While social networking companies such asFacebook, LinkedIn, twitter bagged major part of it.

Additionally, there are a lot of practical business implications for text analytics that include customer experience management, brand monitoring, compliance, business intelligence, and much, much more.One thing that all of the above mentioned business applications have in common is the massive amounts of free form text that no human can possibly read in a reasonable amount of time. Hence, Text analytics is the answer to breaking of unstructured data and unlocking the value of customer feedback.

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