{"id":2789,"date":"2016-08-21T01:30:18","date_gmt":"2016-08-21T05:30:18","guid":{"rendered":"http:\/\/www.servsig.org\/wordpress\/?p=2789"},"modified":"2025-05-10T02:42:25","modified_gmt":"2025-05-10T06:42:25","slug":"understanding-the-language-behind-big-data","status":"publish","type":"post","link":"https:\/\/www.servsig.org\/wordpress\/2016\/08\/understanding-the-language-behind-big-data\/","title":{"rendered":"Understanding the Language behind Big Data"},"content":{"rendered":"

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guest article by\u00a0Francisco Villarroel Ordenes<\/span><\/em><\/p>\n

Unstructured text data from emails, SMS, blogs, online reviews and social media is exponentially growing offering organizations unprecedented resources to monitor brand communications and customer experience feedback (Forbes 2016). This has resulted in the development of an emerging class of research methods using text mining, the process of structuring large volumes of text data to discover explicit and implicit meanings. Text mining methods are currently applied on a wide range of business contexts such as automated sentiment detection from social media, speech recognition in call centers and customer\u2019s keyword search patterns (Forrester 2016). As such, latest news report shows that the market size for text mining (i.e., text analytics) is estimated to grow from USD 2.65 Billion in 2015 to 5.93 Billion by 2020 (Markets and Market 2016). Despite the increasing interest and investments on text mining methods its return on investment is still unclear (Altaplana 2014).<\/p>\n

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Graph 1: Text Analytics Adoption Trends\u00a0(Forrester 2016)<\/p><\/div>\n

As unstructured text data continues to grow, it becomes cumbersome increasing the utilization of text mining across all business disciplines. In fact, service researchers position text mining as one of the key modeling techniques to make sense of big customer data (Rust and Huang 2014). However, there is scarce evidence regarding its utilization in service studies. In this line, I would like to refer to three important factors that could increase the utilization of text mining methods in service and other business disciplines. First, while advances in computer science are providing more advanced methods to analyze textual data (e.g., deep learning); there is a need for a more in-depth theoretical discussion concerning the use of language as a research input. Second, as the number of text mining software and applications increases, it becomes imperative knowing their different strengths and weaknesses. Finally, the current gap in the business curricula regarding text mining (i.e., analytics) leaves a pressing need to the development of ad-hoc teaching material.<\/p>\n

Linguistics lenses in text mining research<\/strong><\/p>\n

Text mining is at the intersection of linguistic communication theories and data mining techniques (Manning, Raghavan and Sch\u00fctze 2008). In other words, any attempt to automatically text-mine big datasets requires a good understanding of language in a determined context (e.g., law, business, etc.) and data mining techniques to extract meaningful and reliable metrics. The utilization of text mining in business research has been driven by the implementation of state of the art techniques, yet little emphasis has been given to the linguistic theories regarding online communications between customers and brands. Closing this gap would contribute to increase the implementation of text mining methods in a wider range of business phenomena. For example, the development of a project about \u201cIrony in Online Service Interactions\u201d would require first having a good understanding of why irony is expressed by customers or employees. Second, it would be important distinguishing between different types of irony and how frequently they are used in a determined context. Is there a difference between sarcastic, humorous, and satirical statements? Finally, it would be necessary the identification of language patterns that characterize different types of ironic statements. This can be done by using linguistic lenses such as the grammar (i.e., syntax) and the meaning of the text (i.e., semantics), the style of words or sentences (i.e., rhetoric), and the context of language (i.e., pragmatics).<\/p>\n

Available Software and Applications<\/strong><\/p>\n

During my research I have tried a number of software and applications that could help service researchers in having a first approach to text mining. Here I will recommend some of the software that has been more valuable for my research:<\/p>\n