Natural language processing (NLP) has created a real boom in the area of text analysis. As part of artificial intelligence, NLP plays a crucial role in social media monitoring, browser’s search results, and smartphone’s assistants that we use on a daily basis. As data that we handle continues to grow, NLP text analysis enables computers to understand and process it.

The NLP field develops all the time, attracting millions in investments and giving rise to hundreds of startups. Managers across industries see the added value to implementation of NLP techniques to the routine work. This allows them to automate manual tasks, save time, and increase the overall business productivity and efficiency.

Text analysis allows extracting meaningful information from textual data, and the usage of NLP plays a crucial role in this process. So, how exactly does NLP work, and which tasks can it do for further processing by AI?

The Nature of NLP and How It Works

Natural language processing plays a role of a mediator between computers and human language. With the help of NLP, computers interpret, understand, and generate human language, creating a feeling that you speak with a human. Natural language processing services, be it sentiment analysis, or text annotation and classification, make the NLP process more accurate thanks to high-quality annotated text or audio data.

Let’s see which tasks NLP can accomplish and how they contribute to the text analysis.

5 Most Common Tasks of NLP

Depending on the application of NLP and text analytics, NLP can be used in different tasks and for different purposes. The most common cases include:

  • Text Classification. This category allows assigning tags or labels to predefined words or word categories for further AI processing. This natural language processing in text analytics helps to organize large datasets and is often used in text categorization or spam detection.
  • Sentiment Analysis. It allows defining the tone of the text, categorizing small pieces of information into positive, negative, or neutral. It moves NLP text analysis to the next level, making it possible to monitor public opinions, customer satisfaction, and overall tendencies among the masses.
  • Named Entity Recognition (NER). NER allows identifying different entities, or categories, from the text. For example, it can group locations, names, or specific classes. In text analytics and NLP implementation, NER helps you to get useful insights from raw data. It’s especially helpful in market and competitor analysis.
  • Information Extraction. With information extraction, NLP makes the raw text more structured and organized. It allows extracting meaningful information, such as facts, events, dates, etc.
  • Machine translation. NLP is the basis for machine translation, allowing translation from one language into another immediately and in real time. Google Translate is the most famous example, used daily by millions.

It’s important to remember that the more precise the training of the NLP model, the more accurate the results will be. It contains several essential steps that cannot be overlooked.

Training Process of the NLP Models

Before an NLP model is applied in the text analysis, it should go through a number of stages, starting with data collection and ending with model deployment and updating. Here are the most crucial steps:

  • Data collection. Depending on the goals of the NLP application, you should first gather diverse datasets that will later be used in the model training. The data should be relevant to the task.
  • Data annotation. This step is one of the most crucial ones. Data annotation implies assigning labels to the unstructured data, making it easier to understand, classify, and process by machines. Data annotation is indispensable for classifying the raw data and making it more comprehensive for further machine processing. It should be done for all tasks and all types of model architectures.
  • Preprocessing. At this stage, you divide big data into smaller pieces, breaking it down into units, cleansing from duplicates, and putting it in order.
  • Model training. Further, you choose the model architecture that will suit your task the most. The most popular ones include transformer models, recurrent neural networks (RNNs), and convolutional neural networks (CNNs). Based on the type of architecture, you’ll continue with the model training.
  • Evaluation. After the training, you evaluate the performance of the model and the accuracy of the final output. Based on such parameters as precision, you compare the expected outcome with the real one.
  • Model deployment. Once all the training steps are accomplished, you can integrate the model into your applications for further text analysis. You can still continue gathering information for further updates.
  • Update. The NLP is a continuous process, which means that the new annotated data can be taught to the models for more accurate results. This is usually done with further application updates.

The combination of the technology advancement and the development of deep neural networks led to the performance improvement of NLP models. This explains its popularity in various industries.

NLP Implementation in Text Analysis Across Businesses

The success of NLP in text analysis came with the implementation of chatbots and customer review tools, spam detectors, and email classifications. The most popular industries that integrate NLP in their daily operations include:

  • Healthcare. NLP assists medical research and is used for analysis of patients’ electronic health records.
  • Customer support. Bots built on NLP help to resolve customers’ queries quicker, saving time and costs.
  • Finance. Analyses built on NLP help with investment decisions, and extract the essential information about reports and financial news.
  • Human resources. Tools based on NLP automate the hiring process, scanning candidates’ CVs and matching candidates with job postings.
  • Legal. In the legal sphere, NLP helps to analyze big amounts of documents, contracts, court decisions, and other records.

Bringing Text Analysis to the Next Level

Natural language processing is a “magic tool” that helps us find a meaningful path in the enormous data that we use every day. The insights derived from the text and information analysis make our decision-making more accurate. The NLP extends our boundaries, making our business more efficient. And new tools, built on the basis of NLP and AI, transform our routine, saving time and efforts.

Whether we train a model for classification, sentiment analysis, or named entity recognition, we process tons of information, structuring our inputs. NLP brought text analysis to the new level, offering us new possibilities and valuable insights.