According to renowned entrepreneurial strategist Paul Brien, visionary decision-making happens at the intersection of intuition and logic. Now the question arises as to why visionary decisions are necessary. For the successful running of any business, entity, or organization there must be some people who make visionary decisions. Now when does the intersection of intuition and logic happen? suppose the decision-makers can correctly predict their upcoming activities or people’s sentiments, and in market scenarios, they can make decisions accordingly. so in these circumstances, the complete analysis of the data is crucial. so, today's topic Text Analytics is one of the data analysis processes that is quite effective and powerful in getting insights from unstructured textual data.
INTRODUCTION TO TEXT ANALYTICS
The process of deriving valuable information and insights from the available textual data. Which in turn helps in enhancing data analysis with textual information
RISE OF TEXT ANALYTICS
There is a vast amount of unstructured data available from various sources like social media, customer reviews, emails, and more which aids decision-makers in making informed decisions, enabling companies to enhance products and services for better customer satisfaction, assessing risks by analysing reports, and preventing of any fraudulent activities.
KEY BENEFITS OF TEXT ANALYTICS ARE AS FOLLOWS
In today’s world as technology improves it is easily available to the last person in society which has given rise to many social media apps like Twitter, Facebook, Instagram, LinkedIn, etc. Hence there will be a huge generation of data and one prominent use is sentiment analysis using Natural Language Processing (NLP) techniques which will help in measuring public opinions, attitudes, and emotions.
Many businesses get insights from customers' feedback across social media platforms, analyzing comments, reviews, and mentions helps in customer preference likes and dislikes which helps in marketing strategies and customer service. By monitoring sensitive topics, potential legal issues, and inappropriate content we can mitigate risks like mob lynching, protests, and riots which are challenging to law and order situations. so, it helps the government also to maintain peace and harmony in the state.
Text analytics plays a crucial role in the healthcare industry. It even increased especially after the COVID-19 pandemic across the world, due to the abundance of textual data generated from patient records, clinical notes, and research papers we can see some key benefits. Analyzing patients’ records helps healthcare professionals in treatment planning, aiding diagnosis, and finding patterns, and potential risks to the health of people. Analyzing research papers, and clinical trial data through text analytics expedites the process of identifying drugs, how these drugs interact with the human body, etc, and in turn discovering new treatments. Improving the administrative process by analyzing feedback, and patients’ surveys to improve service quality, efficiency, and patient satisfaction.
As the major businesses, companies, and global economies are more concerned with the fluctuation of the trends in the market, fraud, investments, and text analytics come in handy to tackle some of the following concerns. Analyzing news articles, social media, and financial reports helps investors to know market sentiment and make informed investment decisions. Analyzing textual data helps in spotting irregularities or fraudulent activities by detecting patterns that might indicate fraudulent behavior. Algorithmic trading is quite evolving in the finance sector which provides for quantitative models aiding in algorithmic trading strategies based on sentiment analysis and news sentiment. Above are a few mentioned key applications of text analytics across various industries. Which varies from trend monitoring to fraud monitoring.
Text preprocessing is a crucial step in text analytics to transform unstructured text data into a formal suitable for analysis has the following procedure
This involves the breaking down of text into smaller units such as words or phrases which enables analysis by getting a structured format.
E.g.: Converting sentences into individual words or phrases
STOP WORDS REMOVAL:
This involves eliminating common words like (the, and, is, an, etc) which have no significant meaning for analysis so that it improves the accuracy of analysis.
LEMMATIZATION AND STEMMING:
This involves reducing words to their base or root form to normalize variants. E.g.: eating, ate to eat
SUPERVISED LEARNING FOR TEXT:
Algorithms learn from labeled data to accurately categorize text into specific groups, aiding tasks like sentiment analysis, spam detection, etc. E.g.: support vector machines (SVM), Naive Bayes, recurrent neural networks (RNNs)
From a large amount of textual data using statistical methods, we will find hidden themes from text by looking at recurring patterns and topics which in turn are used for the content organizations and recommendations. NAMED ENTITY RECOGNITION (NER): In this, we will identify and categorize the entities from the textual data into names, places, and dates which is helpful in chatbots and search engines. In this way, we can classify text into different themes using some of these techniques.
This provides us the textual insights in a visual format
It provides the word frequency based on their occurrence within the text, provides us with the most prevalent terms, and gives us valid insights into key themes.
Using color-coded visuals, heatmaps illustrate trends and patterns in textual data and showcase variations in word frequency and sentiment across texts enabling easy identification of trends within the data. NETWORK ANALYSIS: In this we will map connections and relationships within text data, by this it helps to uncover associations between words, topics, or entities revealing relationships among them.
Even though there are so many benefits and applications of text analytics as we saw above it comes with certain challenges also, As time passes with advanced technological tools we may overcome these also.
Some of the challenges are:
In essence, text analytics can be a transformative tool for unlocking invaluable insights from the vast ocean of unstructured data. By harnessing the power of Natural Language Processing (NLP) and machine learning, businesses can gain a competitive edge, make data-driven decisions, and enhance customer experiences. Text analytics will be part of the new era of data-driven intelligence and strategic decision-making.