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Understanding natural language processing in Data Science

In the present rapidly evolving economy, data consumption is constantly rising at an alarming rate. A major portion of this data is actually in text form. NLP in data science stands for natural language processing and this is a popular category of artificial intelligence that enables data scientists to extract valuable insights from data in textual form. Everything that we express or speak holds great value and information that can be converted into valuable data-driven decisions.

However, extracting this type of data and information with the help of a machine is a complex task since humans speak multiple words, common languages, and tones. All data that humans generate via conversion in their day-to-day lives is in an unstructured form. Because of the advancement of data science, natural language processing, and data analytic techniques, machines can have valuable conversations with humans. The following article will help you to understand natural language processing and the top NLP techniques that are used in data science course

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What is natural language processing?

Natural language processing in data science is an automatic manipulation of different natural languages such as text, speech, and tones with the help of software that enables computing devices to observe, understand, analyze, and eventually derive meaningful insights from human spoken or natural languages. NLP is a popular branch of data science that emphasizes making computers well-trained to interpret our process conversations in a text table format similar to humans. This field is creating methodologies to fill up the gap between human languages and data science. 

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Application of NLP is challenging during the development phase since computers have to interact with humans with the help of programming languages such as Python, Java, etc. that are unambiguous and structured well-trained to analyze spoken, or natural languages that are ambiguous and can change with social or religious changes. 

NLP techniques in data science

Now that you know the meaning of NLP in data science let us delve deeper into the top NLP techniques that are used in data science.

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  • Tokenization 

An important NLP technique that segments the overall text into words and sentences is tokenization. It is a process of segregating text into different segments that are known as tokens. It discards a few characters like hyphens, punctuation etcetera. Tokenization aims to convert text into a simple format that can be convenient for data scientists to analyze. 

  • Lemmatization and the Stemming technique. 

The main purpose of the NLP Stemming technique is to reduce words to their actual root form. The principle behind the stemming technique is that certain words have different spellings but the same meaning can be placed under the same token. In this technique, affixes are usually removed to facilitate efficient processing. Words are converted into lemma in the Lemmatization technique that aims to convert a word having different forms to its basic root form and then group all the words.

  • Stop words removal

In this technique, common words that are frequently used however, adding no or little value to the actual results are removed automatically from the text. This helps in freeing up space and also improves processing time and performance. Stop words removal technique is not used for data analysis as important information can be lost in this technique.

  • Keyword Extraction 

It is a text analysis method that extracts important expressions and words automatically from a text given. This technique helps in summarizing the context of words and texts and then recognizes the actual topic that has to be discussed. Keyword extraction taken in NLP helps to process as well as efficiently analyze data. This technique helps to locate keywords from any text format such as business reports from online forums, news reports, reviews, social media comments, tweets, regular documents, etc.

  • Sentiment Analysis

It is a natural language processing and machine learning technique that helps in examining the emotional tone of users that is conveyed in a sentence or text form. This technique helps to gather and analyze thoughts from opinions and expressions of humans about different topics. The opinions of humans hold great value to governments from corporations and organizations that want to collect information and then make decisions based on these opinions. The emotional feedback or tone of humans that is collected can be negative, positive, or neutral. Sentiment analysis tools are used by businesses and companies to access the sentimental value of humans about their goods, brands, and services and also for customer feedback. Customer sentiments or emotions are evaluated and analyzed with the help of sentiment analysis software.

Case studies and real-life application of NLP

Natural language processing is an AI technique that is empowering businesses and other sectors to transform their operations from data-driven to intelligence-driven initiatives. Several e-commerce platforms are using a smart search provider named Klevu which is based on the NLP technique to offer improved customer experience. It is a highly efficient smart search provider which understands user interaction and performs superior functions like adding contextual synonyms, search autotype etcetera. 

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Analytic vendors and business intelligence units are adding NLP capabilities to their product offerings. Natural language generation and natural language understanding are used for data visualization narration and natural language searches.

MasterCard has launched its chatbot to offer supreme customer support services on the Facebook Messenger app. The main aim of this chatbot is to understand customer data, spending patterns, and user behavior. In this way, MasterCard can provide a better and improved customer experience. 

Future of NLP in data science

As the volume of data is constantly increasing and the requirement of humans to interact with computing devices is also on the rise therefore the need for natural language processing is constantly increasing day by day and this is creating lucrative job opportunities in the market. This shows that there is a great future scope for natural language processing in data science in the upcoming days. 

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Conclusion

NLP plays a crucial role in improving human-machine interaction. In this guide, we have explored different aspects and concepts of NLP like its real-life cases, techniques, definitions, methods, etc. Different companies and business organizations are using data science and NLP techniques to improve their operations and provide greater customer support. If you want to become a data scientist and want to interact with computing devices then you have to acquire linguistic and programming languages. For data scientists and other data science professionals acquiring knowledge and skills such as natural language processing will be valuable for their careers. 

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