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• To achieve a high TF-IDF value, the term frequency (TF) should be high, but the document frequency (DF) should
                 be low. That is, some words may be important for one document but are not common across all the other
                 documents in the corpus.
                 • These numeric values represent the words that need to be considered while processing NLP. A higher TF-IDF
                 value indicates that a word is more significant for distinguishing a document within a given corpus.
              Applications of TFIDF

              Some of the important applications of TFIDF are:
                 • Document Classification: It helps in categorising documents based on their content, such as topic, genre, or
                 subject matter.
                 • Topic Modelling: It helps in predicting the topic of the corpus.
                 • Information Retrieval System: It searches the corpus and retrieves the information based on most relevant
                 searches.
                 • Stop Word Filtering: It helps in removing the stop words from the documents in the corpus so that the data
                 retrieval and processing can focus on words which are important for data processing.

              Natural Language Toolkit (NLTK)

              The Natural Language Toolkit (NLTK) is one of the most commonly used open-source NLP toolkit that is made
              up of Python libraries and is used for building programs that help in synthesis and statistical analysis of human
              language processing. The text processing libraries do text processing through tokenization, parsing, classification,
              stemming, tagging and semantic reasoning.
              Some important NLP tools are:
                 • spaCY: spaCy is a free, open-source library for Natural Language Processing (NLP) in Python. It offers fast
                 processing, pre-trained models, and deep learning integration, making it a top choice for text analysis, chatbots,
                 and AI applications. spaCy provides advanced capabilities to perform NLP on large volumes of text with high
                 speed and efficiency.
                 • Gensim: Gensim is an open-source NLP library designed for topic modelling and document similarity analysis. It
                 is highly efficient for processing large-scale text data and is widely used in machine learning and NLP applications.
                 • No-code: No-code tools make it easier for businesses and individuals to leverage AI without programming
                 skills by offering in-built models and user-friendly interfaces.

                                   Natural Language Processing (Practical)


                       No-Code NLP Tools


              No-code NLP tools enable users to perform Natural Language Processing tasks without programming knowledge.
              Different platforms offer intuitive interfaces for text mining, sentiment detection, and more. These tools are ideal
              for businesses and individuals seeking quick and scalable NLP solutions.
              Orange Data Mining is an open-source, no-code/low-code data visualisation and analysis tool that enables users
              to perform machine learning, data mining, and predictive modelling without coding. It provides a user-friendly,
              drag-and-drop interface that simplifies data analysis workflows.
              MonkeyLearn is an easy-to-use, no-code NLP platform that makes text analysis accessible to businesses and
              non-technical users. Whether you need sentiment analysis, keyword extraction, or customer feedback analysis,
              MonkeyLearn provides a powerful and automated solution for text-based insights.
              MeaningCloud is a No-Code Natural Language Processing (NLP) tool that provides text analytics services.

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