Natural Language Processing (NLP) Techniques & Applications

Natural language processing has revolutionized the way we interact with computers. Thanks to NLP, computers are learning to communicate in a way that is natural for humans. That opens up a world of possibilities for automating tasks, extracting insights from large volumes of data, and creating more interactive and personalized customer experience.

In this blog post, we’ll discuss the most popular NLP techniques and how they can benefit your business. 

_{What Is Natural Language Processing?}

Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that enables computers to understand, process, and analyze human language. NLP techniques can be applied to both written text and speech, so they are commonly used to analyze documents, emails, social media content, and other types of unstructured data. NLP tools can generate responses and their own language-based output, so they can answer questions, provide recommendations, and engage in conversation.

Natural language processing applications can be found in many aspects of our daily lives. For example, NLP enables conversational AI solutions to understand voice commands, helps search engines to provide relevant results, and allows businesses to efficiently process large volumes of data.

Depending on their needs and resources, organizations can choose a prebuilt natural language processing model or build one themselves. Out-of-the-box models don’t require training, but they are usually trained on general data. If you need a more specific niche solution, you can build your natural language processing model with Python. 

_{Natural Language Processing Techniques}

There are many NLP techniques that can help you extract valuable information from text data. Here are the most commonly used NLP tools and techniques. 

_{Stemming and lemmatization}

Most NLP projects start with stemming or lemmatization. The techniques are quite similar, but there are some key differences between them. Stemming algorithms determine the infinitive form of a word by considering common prefixes and suffixes in the analyzed language and clipping them off. While it is rather effective, stemming has its limitations and challenges. Lemmatization digs deeper by analyzing not just suffixes and prefixes, but also linguistics and grammar of the language. 

Reducing words to their base forms makes text analysis more accurate and efficient, making stemming and lemmatization are an important part of the text cleaning process. 

_{Sentiment analysis}

Perhaps the most famous NLP technique, sentiment analysis analyzes a piece of text data to determine whether the sentiment expressed in it is positive, negative, or neutral. In sentiment analysis, every statement is assigned a sentiment score, and these scores are used to calculate the overall sentiment score of a text. 

Sentiment analysis is a versatile and flexible technique that can be used to determine the intent behind a text, its emotion, or the sentiment surrounding a particular topic. That makes sentiment analysis a useful tool for analyzing customer feedback, learning how customers feel about certain features of the product, and extracting actionable insights from customer data. 

_{Named entity recognition}

Named entity recognition (NER) is a technique that names and extracts certain entities from the text. The entities may include people’s names, names of companies and organizations, locations, or dates. After identifying and categorizing the entities, the algorithms determine how many times they occur within a dataset. That allows businesses to discover recurring themes, issues, preferences and customer pain points. 

NER also plays a crucial role in improving search engine results and recommendation systems. By identifying named entities in queries or user profiles, search engines and recommendation algorithms can provide more relevant and personalized results. 

_{Keyword extraction}

Keyword extraction, also known as keyword detection or keyword analysis, is one of the basic NLP techniques. It is a broad term that describes the process of finding the most relevant information in the text and automatically extracting words and expressions that occur most frequently in the dataset. This technique is often used as the first step of the analysis as it makes data analysis faster and easier. In practice, keyword extraction plays an important role in social media monitoring, customer feedback analysis, and search engine optimization. 

_{Topic modeling} 

Topic modeling is an advanced NLP technique built on unsupervised machine learning, which means it doesn’t require training or data tagging. Topic modeling algorithms scan the text and create text clusters around identified keywords and themes. 

While topic modeling can provide useful insights and facilitate data exploration, the resulting topics are based on textual patterns only and do not necessarily represent the underlying meaning of the text. However, combined with other techniques such as sentiment analysis, topic modeling is a great tool for uncovering patterns and themes in customer feedback and customer support data.

_{Text summarization}

Summarization is one of the youngest and most promising NLP techniques. As the name suggests, the technique is used to create a summary of long or complex texts, such as news articles or academic papers. Aside from shortening the text, summarization also minimizes jargon and makes the text easier to understand. 

The process of text summarization can be divided into two parts. In the extract phase, the algorithms extract recurring themes, keywords, and concepts. Afterward, in the abstract phase, the algorithms write a new, simplified text around those keywords. While those approaches can be used separately, combining them is the best way to generate an accurate summary. 

_{Stop words removal} 

In natural language processing, stop words removal is a technique used to eliminate commonly occurring words that carry little to no value or meaning. These words, known as stop words, include articles, pronouns, prepositions, or conjunctions.

The purpose of stop words removal is to reduce the volume of text data and streamline other NLP tasks, such as sentiment analysis or topic modeling. By removing stop words, the algorithms can focus on meaningful words and expressions that can provide valuable insights.

_{Natural Language Processing Software}

There is a variety of natural language processing tools you can use to analyze qualitative data. NLP software can handle tasks like language understanding, sentiment analysis, language translation, speech recognition, text summarization, information extraction, and more. Machine learning algorithms are often used to train models on large amounts of annotated data, enabling NLP software to learn patterns, identify linguistic structures, and make predictions.

Every organization can benefit from a natural language processing API. NLP techniques allow businesses to save time on data processing while also making the analysis more effective and scalable than ever. As an NLP-powered data analysis solution, Essense can process and analyze your customer feedback in seconds, providing you with common feedback topics,and valuable insights for your business.

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