Text analytics is the technique of procuring meaning from written communication. In a specific customer experience context, text analytics indicates examining text from the potential audience. You will be capable of finding the topic of interest and patterns, after which you will take practical action, according to the learning.
It is possible to conduct text analysis manually. However, it is not an efficient process. Hence, data analytics solutions are created with NLP algorithms and text mining aid, which is useful in seeking the meaning within massive text amounts.
Online reviews, emails, survey results, notes of call center agents, tweets, and different kinds of written feedback will be holding insight into the potential audience. A plethora of information is present in different recorded interactions, which can be converted into text easily. Text analytics contributes to being the option to unlock and find the meaning from unstructured text.
It provides the options to reveal the themes and patterns. So, you will be capable of understanding what the potential audience is thinking. Besides this, text analytics software issues early trouble waning as it showcases what the potential audience is complaining about. The use of text analytics offers valuable information from the data, which is quantified easily.
It is useful in turning unstructured customer thoughts into structured data. The business organization can reap a plethora of benefits from this type of structured data. As you go through this article, you can know different aspects of text analytics:
Data analysis
In the beginning, it is possible to conduct data analysis with text analytics solutions. Different types of pre-trained machine learning models are available in the market, which are useful in conducting text analytics. It is possible to create personalized machine-learning models for the extraction and classification of text.
It is a suitable option if you want to identify different keywords and topics in a certain field. By undertaking a series of sequential processes which comprise choosing an appropriate type of model, importing one’s data, specifying distinct categories, educating the model, and implementing a trained model for new data analysis, one can produce a customized model.
Use of Business Intelligence tools for understanding the data
After the completion of text analysis, it is a prerequisite to generate the data visualization results. Different business intelligence solutions are available in the market, which offer a helping hand in generating interactive and attractive charts and reports that are useful in communicating the data’s primary insights.
Text analytics is not the same as a search
This is the process of unstructured text analysis, and extraction of the relevant details, after which it is transformed into structured details, which are used in different ways. Text analytics involves the extraction of text. Besides this, search means the retrieval of the document. It is possible to augment search text analytics.
Text analytics is useful for the extraction of different types of information.
It is possible to extract the text’s typical information types that are inclusive of the entities, terms, sentiment, and concepts. Various vendors make the right use of various terms for describing such type of information. There are a plethora of vendors that speak about events, topics, themes, and facts. You should understand what every vendor is offering.
It is possible to analyze data or combine it with structured data.
Business organizations that make the right use of text data will integrate it with different regular data sources for analysis. It is another data form. Analysis of the textual data without merging the same with different data in the systems is known to be quite informative.
It is possible to analyze the social media data properly. There is a wide assortment of business organizations generating different predictive models with the aid of text data. It depends on the type of data you should analyze. Besides this, it depends on the types of issues you intend to resolve.
It is necessary to take the taxonomy into account.
Taxonomy is the process that helps in organizing information into different hierarchical relations. It is essential for text analytics, primarily when dealing with different vocabularies in specific industries. The taxonomy makes the best use of alternate expressions and synonyms.
A bunch of vendors claim to offer baseline taxonomies out of the box. However, it does not mean that they are going to work out of the box. There are various vendors who are going to tell you that taxonomy is not necessary. You might avoid creating a taxonomy for specific subjects. However, you should be prepared to interact about what is out of the cool for the creation of the categories.
Summary
Text analytics is a suitable choice for the business organization to seek meaningful information across a plethora of data sources, such as social media interactions and customer support tickets. With the aggregation of the text analysis results and the use of business intelligence tools to turn the tools into easy-to-understand graphics and reports, advanced data analytics solutions are useful in recognizing the trends, patterns, and actionable insights useful in making different data-driven decisions.
With the analysis of customer feedback and examining customer support ticket content with different text analytics tools, it is possible to leverage such results with the aid of text analytics. It is useful in diagnosing different opportunities for improvement and adapting the services and products as per the client’s expectations and needs.
It is possible to get started with text analytics easily. A wide assortment of text analytics online is available in the market, helping to conduct text analytics and visualize the results. Text analytics provide a suitable choice for the business organization to find meaningful information across a plethora of data sources, such as social media interactions and customer support tickets.
Author: Muthamilselvan is a passionate Content Marketer and SEO Analyst. He has 7 years of hands-on experience in Digital Marketing in IT and Service sectors. Helped increase online visibility and sales/leads over the years consistently with my extensive and updated knowledge of SEO. Have worked on both Service based and product-oriented websites.
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