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3 Ways to Experiment with Text Analytics

Billy Yann
Deep learning and machine learning specialist, well-versed with experience in Cloud infrastructure, Block-chain technologies and Big Data solutions.
February 25, 2021

Text Analytics

Quantitative data uncover trends, insights, and patterns. Text analytics automatically translates large volumes of unstructured data into quantitative ones. By combining text analytics with data visualization tools, businesses begin to understand the true story behind numbers. This process aids them in better forecasting & making the right strategic decisions.

Text analysis & text mining often get used with text analytics quite interchangeably. An ultimate goal is to analyze all unstructured data available for obtaining the right insights. Text mining & text analysis provide data of qualitative nature and the text analytics quantifies them through visualized charts & reports.

Text analysis tools are perfect for a sentiment-analysis or a topic-detection. Text analytics leverages the results of text analysis for identifying any emerging patterns. It provides any actionable insights for making the right improvements.

Text Analytics useful to Businesses

With a colossal amount of unstructured data generated every minute, text analytics becomes more valuable. Businesses are able to automatically extract all kinds of meaning from the unstructured data at hand by turning it into quantitative insights. All businesses develop good strategies & improve customer satisfaction through text analytics. They go on to detect product issues, conduct market research, and monitor brand reputation.

Benefits of Text Analytics

Text Analytics is scalable: Able to analyze large volumes of data in a short time. It gives you the apt results in real-time.

Businesses make confident decisions thereby resolving issues in a timely manner.

Examples of Text Analytics

An interesting application of text analytics in business: Customer Feedback Analysis. It involves a proper analysis of product & service reviews for observing how the customers evaluate a company/firm/organization.

The analysis includes processing all results of open-ended responses to customer surveys. Public opinion about a brand in social media helps with a proper Customer Feedback Analysis.

For example, for analyzing the NPS open-ended responses, businesses run a topic analysis, which is a text-analysis technique. It automatically tags NPS responses based on certain predefined categories.

Text Analytics: How to get started?

Analyzing the Data

The very first step would be to use the text analytics tools for analyzing your data. In most cases, pre-trained machine models are used in performing the text analysis right away. A Sentiment Analysis helps with understanding how text analytics work.

All businesses can build their own customized machine-learning-tools for both text classification as well as extraction. It offers the best option if you are looking for certain keywords or topics within a specific field.

A Custom Model in 5 Steps

A type of model gets chosen, either a classifier or an extractor. The data gets imported by using a CSV or Excel File/third-party integrations. All tags for data analysis get defined properly for reference. Then, the model gets trained by manually tagging several other examples. Referring to examples, the model makes its own predictions.

Keep training the model for accuracy of results if you are not satisfied with the output. Upload new data as a single batch while using one of the third-party integration apps for automatic analysis of data.

Business Intelligence tools to understand the Data

Once the text analysis gets completed one can create a data visualization of all results. Some of the Business Intelligence (BI) tools that are used include Google Data Studio and Looker among others. Attractive & interactive reports, as well as charts, are made to communicate the main insights of the data at hand.

A Small Inference

Text analytics offers companies/firms/organizations meaningful information across a variety of data sources. These might vary from customer support tickets to social media interactions.

Business Intelligence tools are used effectively to aggregate the results of text analysis. One can easily turn the numbers into easily understandable figures, charts, or graphics. Text analytics do identify patterns, trends, and forecasts along with actionable insights that get used in making data-driven decisions.

Analyzing customer feedback through NPS responses & product reviews along with content-examination of customer support tickets using proper text analysis tools helps leverage apt results using text analytics. It helps you detect opportunities for improvement while adapting your product or service with regards to your client's needs as well as expectations.

3 ways to experiment with text analytics


As mentioned, text analytics gets referred to as text data-mining & its analysis for uncovering any insightful as well as actionable information and trends along with patterns from text. All the extracted & structured data is much more convenient than the original text. This makes it easier to determine an information's data-quality as well as its usefulness. All developers & data scientists use the mined data in downstream data visualizations, analytics, machine-learning, and other related applications.

The purpose of text analytics is to identify facts, sentiments, relationships, and other contextual information. It advances to topic-assigning, determining categories, and discovering apt sentiments. Establishing a relationship pattern between more than one entity & any qualifiers form a key capability of text analytics.

Data Extraction from documents vs. form-fields

DevOps & AIOps form two areas that gained considerable momentum in the industry according to Gartner's Hype Cycle for Performance One of the hardest challenges in the field of text analytics is the processing of enterprise repositories as well as large documents. These large documents might be corporate SEC filings, electronic health records, or even aggregated news from websites. Unstructured & semi-structured documents pose their own challenges as well while undergoing the process of text analytics.

Parsing documents pose some unique challenges. A document's size, as well as its structure, dictates domain-specific pre-processing rules along with NLP (Natural Language Processing) Algorithms. Larger documents often require validating the extracted information based on various contexts.

Simplified algorithms are required when it comes to performing a potentially simpler task of extracting information from a form-field or other short-text-snippets. This is because the text fields are identifiable, short, and carry a specific type of information.

For leveraging unstructured field data in an application or including insightful information from the text in data visualization, text analytics form the all-important first step. The Agile Data Science teams often use Spikes for conducting discovery work. The team needs tools, skills, and methodologies for performing proper text analytics.

Effective use of a Public Cloud's NLP & Cognitive Services

As is known, the major public clouds offer natural language processing along with other cognitive services. Teams working in these environments are skilled in using these algorithms. Options available for such teams:

Azure Cognitive Services - Form-Recognizer extracts key-value pairs from text fields & documents. Text analytics identify sentiments, entities, and key phrases, A more advanced Language Understanding Capability gets used for developing NLP models in mobile & IoT applications.

Google Cloud Platform - Developers use the natural language API for analyzing basic entities, extract sentiments, and categorizing content into a lot of pre-defined categories. An AutoML Natural Language creates custom categorization & apt sentiment models.

AWS (Amazon Web Services) - Similar text analytics & NLP features with APIs (Application Programming Interfaces) get used for detecting entities, events, key phrases, sentiments, topics, and personally identifiable information. Amazon SageMaker can be used for testing, training, and deploying NLP models such as BlazingText, BERT (Bidirectional Encoder Representations from Transformers), and SpaCy.

IBM Watson Natural Language Understanding - Extract entities, sentiment categories, and concepts along with sophisticated features that identify relations, and semantic roles.

Text Analytics Tools in Data Integration & Machine-Learning Platforms

With organizations invested in data integration, machine learning, and analytics platforms, they need to have some text analytics & NLP capabilities. Some platforms give options that are easier & faster in performing lightweight text analytics rather than coding to APIs. Some of these platforms include:

Alteryx Designer - Text mining functions for pre-processing, topic modelling, and Sentiment Analysis.

IBM SPSS Modeller Text Analytics - Used for categorizations.

SAS Visual Text Analytics - Visual tool & open platform for parsing, information extraction, NLP modelling, Sentiment Analysis, and Trend Analysis.

Data science platforms offer text mining functions natively through plug-ins & interactions with public cloud services - RapidMiner, Knime, and Dataiku.

Specialized Text Analytics Tools

Some tools for simple text Analytics - Lexalytics, KeatText, MeaningCloud, NetOwl, Rosette Text Analytics, Provalis Research, and MonkeyLearn. Text analytics is common in customer experience, market research, marketing automation, social listening, and other platforms that capture qualitative information around customers & sales prospects.

For starting with text analytics, any data & analytics discovery exercise gets defined by various questions as well as target outcomes that potentially deliver the apt business value. The overall complexity of content, document, and text fields that need processing gets examined along with the target entities, topics, and semantics. Understanding the problem complexity helps which text analytics tool to use. Recognize that text analytics & NLP are a form of machine learning. For all businesses trying to improve customer experiences, text analytics is essential to develop.


It is fun to experiment with ways that aid lightweight or simple text analytics tools to work perfectly for analyzing your data. Businesses grow & evolve well through the best text analytics tools as well. Let's experiment & prepare the best strategy that works well for you!