In today’s digital scenario, data plays an important role. Extracting significant insights from vast amounts of text becomes imperative. Natural Language Processing introduces a wide range of strategies to provide valuable knowledge and added value to businesses, researchers, and professionals. The unstructured data is used for analysis on a large scale. These techniques are applied in businesses to operate in different fields such as finance, healthcare, marketing, etc. This ever-increasing data volume grows NLP-based solutions significantly.
In this blog, we are justifying how NLP’s eight powerful techniques are going to revamp raw data into meaningful information.
What is Natural Language Processing?
In computer science, Natural Language Processing plays a crucial part in artificial intelligence. Benefits of NLP include different computer facilities such as enabling them to understand human language through text or speech form. The primary features utilized for computers here are computational linguistics, statistical models, and machine learning. So that computers are able to comprehend the meaning, intent, and sentiments like humans do.
Here let’s explore the main NLP techniques that are widely used in text analysis and language understanding processes.
Tokenization
The name says it all. To give a better definition we can say, it is the process of breaking text into smaller units. These divided parts are called tokens. Words, sentences, and phrases are the perfect example of Token. Tokenization processes involve turning large text chunks into manageable units for better analysis.
Why It Matters:
- It splits text for easier access and comprehension.
- It serves as a footing for other more complex NLP processes including sentiment analysis, and machine translations for different languages.
- For instance, the tokenization technique helps classify big data customer reviews with regard to satisfaction, delay, or recommendation.
Statistical Impact:
Based on a study by Gartner, by 2025 more than 80% of the enterprise data will be unstructured data, and thus tokenization has to be the beginning of analysis.
Named Entity Recognition (NER)
Named Entity Recognition (NER) is an NLP technique aimed at searching and categorizing important objects in the text into certain categories. These components include a person’s name, organization name, location, dates or times, etc.
Why It Matters:
- NER is used to obtain important signals of a certain type, for example, names or dates, from the large corpora of texts.
- It is especially useful in fields like fraud detection since names, locations, and events need to be searched to locate suspicious activity.
- While using NER in a customer service situation it can highlight specific products and issues. The issues for which customers want solutions and look for organizations to respond promptly.
Statistical Impact:
A study from Forrester shows that companies using NER in customer service have seen a 25% reduction in response time, significantly improving customer satisfaction.
Sentiment Analysis
Sentiment Analysis lets computers recognize the emotional tone of each word. It will consider the response in the text -for instance, whether it is negative, positive, or neutral.
Why It Matters:
- Companies come to know how customers feel about their product offering or brand.
- This tool’s efficient use makes things easy such as brand management and handling customer feedback and criticism.
- Theoretically, sentiment analysis can scan millions of tweets or posts to locate trends, for example dissatisfaction concerning delivery of products or satisfaction concerning customer service.
Statistical Impact:
As per McKinsey’s study, feedback’s automated sentiment analysis increases 30% in customer satisfaction for companies.
Topic Modeling
Topic Modeling does not focus on just the explicit topics but finds more, unknown latent topics in a large data set. It defines issues that appear in the set of documents within a specific domain. Among a wide range of approaches, the most frequently used approach is Latent Dirichlet Allocation(LDA) which gathers words into topics.
Why It Matters:
- It organizes information, which comes in large amounts then understands the underlying themes.
- It supports mostly the publishing and legal industries, which use massive textual data.
- Topic modeling can also be used by a company to dissect data received from customers in relation to various products. For example, retailers will monitor things that matter to customers most such as a number of “delivery time” and “product quality”.
Statistical Impact:
Harvard Business Review has reported that Topic modeling has enhanced the current rates of predictive accuracy incorporating the financial data in the forecast by 20% for market reports and news data.
Text Summarization
Text Summarization involves creating short versions of long documents and simultaneously maintaining important details. There are two main approaches: There is the extractive method, which gets the best phrases of the text, and the abstractive, the latter of which requires understanding the context of the displayed text.
Why It Matters:
- Summarization makes things easy to read through long reports, research papers, or news articles.
- Geographically, it can help decision-makers save time in their decisions by bringing to their notice only the most relevant information. For instance, a legal firm can apply text summarization to scan through large contracts or judgments and abstract out main points for action.
Statistical Impact:
According to a Pew Research survey, the adoption of the kits to work with artificial smart assistants boosted employee performance through the reduction of time taken to analyze documents by automating the summaries by up to 40%.
Tagging Part-of-Speech
When you tag the input tax, Part-of-Speech Tagging or POS plays an important role. Or it can tag the whole sentence with the appropriate part of the speech support. It is desirable due to its effective utilization of the grammatical structure of the text.
Why It Matters:
- POS tagging is applied in machine translation, speech recognition, and question answering.
- It improves the performance of models by pointing out the role of a word in a given sentence. For instance, in a health care context, POS tagging is useful in differentiating between diseases (or parts of the body where diseases affect) – nouns and treatments – verbs by extracting simple facts about them from health records for use by doctors.
Statistical Impact:
According to a Search Engine Journal survey, POS integration tagging in search engines helps to improve search relevance by up to 15%.
Dependency Parsing
Dependency Parsing focuses on the dependency of one word on another word in a specific sentence. It deals with the manner in which words are linked grammatically to one another, to show the pattern of a sentence.
Why It Matters:
- They are important for understanding complex sentences along with clearing ambiguity.
- They are useful when helping to enhance the credibility of chatbots, translate languages using artificial intelligence, and answer questions through the assistance of artificial intelligence.
- In customer service, dependency parsing can be of great use to a chatbot; it will be able to parse through complicated inquiries and generate suitable replies. For example, a customer saying— ‘The product that I bought is not functioning as expected, what do I do to get a different one?’ entails interconnections of activities, outputs, and states.
Statistical Impact:
Organizations adopting dependency parsing in chatbots reported a 20% improvement in customer query resolution according to a Gartner report.
Text Classification
Text Classification is an AI technique that automatically assigns a set of documents into predefined categories. The categories are based on various features such as sentiment, topic, or language.
Why It Matters:
- People apply TM for spam, document, and language classification among others.
- Text classification can be very valuable by defining processes such as email, social media posts, or document sorting based on their content.
For instance, an e-commerce company to analyze customer reviews to such categories as ‘‘product complaints,’’ ‘‘delivery issues,’ or ‘‘recommendations’’ were backed as a program that enabled groups to address specific observations.
Statistical Impact:
Accenture reported that text classification adopted by different companies helped organizations reduce costs by 30% due to fast decisions and automation.
Using NLP to Drive Business Success
NLP is not just a technology craze, but a business solution. In customer relations and employee satisfaction, in legal proceedings, and in summing up large volumes of data and information, there are a plethora of advantages to NLP techniques.
Kodehash and NLP in Action
At Kodehash, we use the best-of-breed NLP tools to enable business leaders to make informed decisions based on their data. Depending on the case, it enhances customer communication through the usage of chatbots or introduces the automation of document analysis based on the NLP methods converting the initial data into comprehensible information. Professional knowledge thus enables any firm to form strategies, and exploit their unstructured data effectively while countering existing and emerging competitors.
Final Thought
Many businesses are dealing with vast volumes of textual data that are unstructured in nature, and it is where the solution based on NLP methods is useful. By using tokenization, NER, sentiment analysis, and more, the advantage is given to businesses for making improved decisions. Companies that implement these approaches will be ready for data mining with a company like Kodehash to maximize the potential of their resources for success in a digital world.