Natural Language Processing (NLP) and text analysis are technologies that enable computers to understand and interact with human-written language. They have wide applications in business, data analysis, and communication. Let's look at them in detail.
Text analysis
Text analysis is when computers read human-written text to find useful information for businesses. Special software helps computers find trends, patterns, feelings, and connections in the text. It's used to quickly and accurately handle lots of text, like emails, reviews, social media, and documents.
Text analysis, sometimes called text mining, helps data scientists and analysts determine if content is important for certain topics. They use special software created by computer scientists to do this.
Natural Language Processing (NLP)
Natural language processing is the technology that allows computers to read text. This is done by simulating the human ability to interpret spoken words or a natural language such as English, Hindi, or Chinese. Natural language processing does both natural language understanding and natural language generation. Natural language generation means giving computers the ability to develop natural language text. For example, to take part in a dialogue.
As a technology, natural language processing has been popular for the last ten years. Products like Siri, Alexa and Google's voice search use natural language processing. They use it to understand and respond to the user's requests. Sophisticated text analysis applications have developed in multiple fields. For instance, there's one used for fraud detection in the insurance industry. Medical research, contextual advertising, risk management, customer care, etc., are some of the other fields.
Today's natural language processing can consistently evaluate large volumes of text-based data without bias. They can interpret concepts that lie within complex contexts. They can also decipher language ambiguities to draw key insights or provide summaries. Given the daily production of huge amounts of unstructured data, this technology is vital for efficiently analysing text-based data.
Python for natural language processing and text analysis
As mentioned, natural language processing is part of AI focused on understanding human-generated text. Industry experts prefer Python for processing text data because it offers numerous benefits.
Python is easily understandable. Additionally, it resembles pseudocode, which makes creating and testing code easier. Also, since the level of abstraction is high, it facilitates the creation of natural language processing systems. Because of the simplicity of Python programming language, people can focus on natural language processing more. Otherwise, they will have to give more attention to the details of the programming language. The efficiency of Python programming helps in the quick development of natural language processing prototypes.
Python is great for building natural language processing systems due to its strong community support and widespread use. Python offers open-source NLP libraries and various machine-learning libraries like TensorFlow, PyTorch, Apache Spark, and more, all with Python interfaces.
Python excels in handling strings and files, making tasks like splitting a sentence at spaces easy with just one line of code. Its robust string processing capabilities and the AI ecosystem and machine learning libraries make Python programming the perfect choice for building natural language processing systems.
Popular natural language processing libraries in the Python programming language
Python has several popular natural language processing libraries. These offer a wide variety of functionalities for the processing and evaluation of text. The following are some of the most commonly used natural language processing libraries:
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Natural Language Toolkit (NLTK)
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spaCy
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TextBlob
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Gensim
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Sci-kit-learn
Key takeaway
Text analysis means computers understand human-written text, while natural language processing is the tech that helps with this. Companies use it to understand and reply to user requests, find important trends, and uncover hidden insights in big data. This helps them make smart decisions about what they're good at and where they need improvement.
Data scientists and analysts should be at the top of their game to provide these valuable insights to the company. Companies are looking for individuals who can contribute to the organisation's growth in this way. Imarticus Learning has collaborated with iHUB DivyaSampark @IIT Roorkee to deliver a data science course that will teach the fundamentals of data mining and machine learning with Python programming language. Visit Imarticus Learning now to learn Python online.
