Text Mining and Text Analytics Paper Example

Introduction

The digital explosion has lead to the collection of robust structured and unstructured data that cannot be analyzed by the traditional data analysis software. It is therefore important to use advanced techniques to process and analyze data. Text mining and text analytics are particularly more effective in analyzing the robust mix of statured and unstructured data. Text mining helps in the extraction of numeric indices from a text or documented to make them accessible and easy to process using different algorithms (Neha, 2012). Clusters of words as well all general terms can only be analyzed after the texts are turned into numbers of data analytics.

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Advantages and Disadvantages of Text Mining and Text Analytics

Advantages of Text Mining:

Text mining, especially in web mining, enables websites to carry out user analytics and offer personalized advertisements without the effort or input from the website owners

Text mining can also be used by the government to classify and analyze the threat matrix because it has a higher predictive capacity that the traditional dining analysis.

Companies such as PayPal, Amazon, and eBay uses text mining to query user's searches and personalize advertisement of different users thereby improving the customer relationships and customer satisfaction.

Text mining can also be used in old documented to understand patterns of historical text and languages analyzed using natural language processing to identify patterns in the texts thereby improving communication.

There are advanced machines that can be used to access text, copy, analyze and annotate the texts then relate the text to existing information and improve understanding. These machines enable annotation of copyrighted documented without an infringing the copyrights. Information retrieval systems have been effective in analyzing digital texts that were not possible in the previous years (Miller, Walker, Landsman-Blumberg & Cuyun Carter, 2013)

Unstructured data can be processed through information extraction to extract useful actionable data into useful information. Facts extraction is used by companies such as Google and security companies to extract information from intercepted terrorist's messages to thwart those threats (Ghosh, 2016).

Natural langue processing can help in tagging parts of speech to determine the missing parts and enabling complete reporting. Additionally, Word sense disambiguation and parsing have been effective in disambiguating text and providing an accurate representation of facts or data.

Disadvantages of Text Mining:

Web mining and text extraction may be used by unauthorized extremists group for illegal purposes. Therefore, Web mining is still illegal in many countries, especially when used to breach proprietary rights and copyrights.

Over the past five years, most companies and countries have amended their laws to covers ethical issues around web mining which makes unauthorized web mining an invasion of privacy.

Countries such as America used data mining and information extraction the profile people online and this has ethical implication as it involved data capture and extrajudicial profiling. Therefore, whether data mining, information extradition, natural language processing, it is important to understand that there are ethical and legal implication as well as regulation that governed the use of these advanced text mining techniques.

Conclusion

Use text mining and text analytics will increasingly become more useful in all fields from academic, research government agencies and personal use. However, it is important to understand the cost and potential harm of the technology. There should be proper structures in place for governing the use of text mining technologies avoid abuse. Both the benefits and disadvantages of text mining should

References

Ghosh, M. (2016). Case Study: Text-Mining Customers View Point and Perceived Value About Brand. International Journal Of Business Analytics And Intelligence, 4(1). Doi: 10.21863/ijbai/2016.4.1.016

Miller, P., Walker, M., Landsman-Blumberg, P., & Cuyun Carter, G. (2013). Using text mining of electronic medical records to identify Kras testing status in patients. Value In Health, 16(3), A21. Doi: 10.1016/j.jval.2013.03.128

Neha K, S. (2012). Introduction of Text mining and an Analysis of Text Mining Techniques. Paripex - Indian Journal Of Research, 2(2), 56-57. Doi: 10.15373/22501991/feb2013/18