Introduction To Text Analytics



Text analytics has recently become prominent solutions to deliver valuable business insights in a big data environment. Big data deals with three different types of data and they are unstructured, structured and semi-structured.


What is Text Analytics?

Textual data comes under Unstructured data and it is is different from other two types of data because it cannot be organized into numeric fields or analyzed using traditional business intelligence software. 

NLP is a necessary means to facilitate text analytics by establishing structure in unstructured text to enable further analysis. Through text mining, we take a large collection of text data and try to derive useful information by analyzing it. The text can be represented in many different ways like character, word, phrase, parts of speech, sentiment, etc... BluePi is proficient with some of the most advanced technologies including like Python, NLTK (Natural Language Toolkit), POS Tagger (Part-Of-Speech Tagger), Scala, Apache Spark’s MLlib (Machine Learning Library).


Text analytics can be processed in seven steps:

1)Text Identification

2)Text Mining

3)Text Categorization and Clustering

4)Search Access

5)Link analysis and Sentiment Analysis

6)Summarization

7)Visualization i.e., generating graphs and reports

Applications of Text Analytics:

1. Social media data analysis: - Text analysis can analyze a large amount of unstructured data, extracting opinions, emotions, and sentiment to predict customer behavior.

2. Email spam filters: – To determine the advertisements or promotional or phishing and unwanted material.

3. Competitive Intelligence: - Which means understanding and learning what's happening in the world outside your business so you can be as competitive as possible.

4. National security and intelligence: - Many communicating devices are operated on the internet throughout the world which contributes increase the risk of Internet-based crimes. Text mining intelligence and anti-crime applications are making internet crime prevention easier for any enterprise and law enforcement or intelligence agencies.

5. Customer care services: -Text analytics is adopted to improve customer experience using data from different sources valuable information such as surveys, trouble tickets, and customer call notes to improve the quality, effectiveness, and speed in resolving problems in return which helps to build loyalty and growth of market share.

6. Workforce analytics: -Taking the opinion of employees of an organization i.e., one(manager) can know how employees are feeling about their companies, managers and work environment.

Example:

We can make the systems in such a way that if some panic conversations happen in social media like Facebook, tweets on Twitter, etc… authorities can monitor and take respective action in such cases.

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