Text Processing can transform the unstructured data into insightful information with the help of machine learning models.

Organizations are now clamouring data to improve their businesses. An increase in Demand for customer-services has prompted the organizations to generate and utilize data on an everyday basis. Big data heavily governs the functioning of any organizations. But the humongous amount of data retrieved by organizations is unstructured and unsegregated. This creates a significant challenge for organizations to get an insight into the functionality of their businesses. So, to rectify this major problem, Text processing comes in handy. It is a technique associated with Natural Language Processing, which, like NLP, transforms the unstructured data into insightful information.

Understanding Text Processing

Every day we as consumers use apps to seek options that can enhance our experiences as customers. We are flooded with information every day. And this information is retained by organizations in the form of data, so that powerful insights can be drawn out to enrich the businesses.

However, like said earlier, this information is unstructured and needs to be reformed into insights for any business to function diligently. The technique of Text Processing, with the help of machine learning models, transforms the unstructured data into analysis, manipulation, and generation of texts. It is a part of the NLP, and unlike NLP, doesn't involve understanding the texts that it is working with.

For example, while generating any emotional text, the Text Processing only analyses the positive and negative sentimental words in a sentence with the help of its machine learning models, whereas the NLP has the ability to transform full sentences into languages.

Methods and Techniques for Text Processing

Text Processing, also known as Text mining, applies different techniques for analysing the given set of information. These are:

• Word Frequency– Word Frequency involves identifying the frequently used words, terms or concepts in unstructured data to gain powerful insight. For example, in an online retail business, the 'filter' option would help the businesses gain an insight into what the customer is looking for.

• Collocation– Collocation refers to the sequencing of words that commonly appear near each other. Some of the common types of Collocations are bigrams ( a pair of two words such as 'get started,' 'decision making'), and trigrams (a combination of three words such as 'keep in touch'), which helps in increasing the granularity of text, improves the semantic structure of the text and helps in accurate text processing.

• Concordance– Concordance recognizes a particular context, which enables the appearance of a word, or set of words, to understand the exact meaning of the context of those words.

Classifying the Text Processing

Text Classification

With the help of Text Classification, tags can be assigned to the unstructured data, so that businesses' can analyze all sorts of information, thus speeding up retrieving insights in a cost-effective manner. The following  methods help in text-classification:

• Topic Analysis–  With the help of topic analysis, the organizations and businesses can understand the main themes or subjects of a text, which will improve the organization of text data. For example, the product issues can be identified by the businesses when the customers review the Product as not what was expected.

• Sentiment Analysis–  With the help of sentiment analysis, the emotions ingrained in the text can be identified. Sentiment analysis is not only lucrative for businesses to understand the changing pattern of a customers' behaviour, but the customers are also presented with options to choose from. This type of analysis is observed in the reviews of any products, understanding the opinions and feelings in a text, and classifying them as positive, negative, and neutral.

• Language Detection– With the help of language detection, the text can be classified on the basis of the language. This is usually observed when the business' provides services to its customers on a geographical basis. This helps save some valuable time and helps in understanding the geographical pattern of customers' behaviour.

• Intent Detection – With the help of Intent Detection, the intent behind the text processing can be recognised. This is particularly used in analysing the intent of the customers, while interacting with them.

Text Extraction

Text extraction utilizes the technique of analysing only specific words, sentences, or data in a text, so that the text processing becomes fast. It applies the following methods:

• Keywords Extraction– With the help of Keywords Extraction, the customers are presented with only those options that are paramount to them. It allows the businesses for indexing data that needs to be searched, summarizing the content of a text, or creating tag clouds.

• Named Entity Recognition– Named entity recognition allows the business' in identifying and extracting the information about the companies, organizations, or persons from a text.

• Feature Extraction–  With the help of feature extraction, the characteristics of a particular product can be identified.

Applications of Text Processing

Just like NLP, Text Processing has a wide range of applications amongst organizations. Some of the applications are listed down below:

• Risk Management– With the plethora of data available, organizations are often at risk of a possible cyber-attack or data infringement. Integrating Text Processing with risk management Software would help manage the unstructured data by mitigating the risk of data infringement. It will also help link the essential information together so that the right information can be available at the right time. This is specifically useful in the financial sector like banks, where the customer information is paramount in managing their loans and bank accounts.

• Knowledge Management-The collection of data in organizations is huge. One of the major challenges, observed with this huge amount of data, is retaining the knowledgeable information. By integrating the Text processing with the Knowledge management software in the system, will help organizations in retrieving a clear and reliable data from the glut. This can be utilized in the healthcare industry, where knowledgeable information is necessary for understanding the diagnosis, treatment, and prognosis of the diseases.

• Preventing Cybercrime- Data is anonymous, and organisations are at a higher risk for falling as prey of cybercrime. By integrating Text processing, along with the many applications of cybercrime, will help in preventing the organizations from being a potential victim of fraud.

• Customer Services– By integrating the text Processing in the online surveys, the organizations will be enabled to operate on the basis of the changing customers' behaviour and pattern. This will also help organizations in utilizing different sources for enhancing customer experiences.

Thus Text Processing applies a leap of faith amongst organizations for analysing the text to improve the customer services and enhancing the business operations.