Category: NLP
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Sentiment Analysis: Unveiling the Power of Text Analysis
In the era of big data, understanding customer sentiment is crucial for businesses to make informed decisions. Sentiment analysis, also known as opinion mining, is a powerful technique that helps businesses extract valuable insights from text data. Whether it’s understanding customer feedback, monitoring social media chatter, or analyzing product reviews, sentiment analysis can provide invaluable…
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Unraveling Text Classification: Traditional Approaches with Scikit-learn
Welcome to a journey into the world of text classification, where we’ll explore some traditional yet powerful approaches using Scikit-learn. While deep learning has taken center stage in Natural Language Processing (NLP), these classical methods remain quick and effective for training text classifiers. Our playground for this experiment is the 20 Newsgroups dataset, a classic…
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Unveiling the Power of Word Embeddings with Gensim
In the realm of Natural Language Processing (NLP), word embeddings have emerged as a game-changer. Unlike traditional approaches that use words as features, word embeddings leverage dense, low-dimensional vectors to capture the meaning and usage of a word. One pioneering model in this domain is Word2Vec, developed by Thomas Mikolov and team at Google. In…
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Visualizing NLP with Pretrained Models – spaCy and StanfordNLP
Natural Language Processing (NLP) is a crucial aspect of understanding and processing human language using computational methods. In this tutorial, we will explore two popular NLP libraries – spaCy and StanfordNLP – and demonstrate their capabilities using pretrained models. spaCy – English NLP Let’s start with spaCy and an English example. We’ll use a snippet…
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Exploring Named Entity Recognition with Conditional Random Fields
Named Entity Recognition (NER) is a fundamental task in natural language processing that involves identifying and classifying entities, such as names of people, organizations, and locations, within a text. NER plays a crucial role in various applications, including information retrieval, question answering, and text summarization. In this blog post, we’ll dive into the world of…
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Exploring Data with Sentence Similarity: Unveiling Insights with NLP
Unlocking the Potential of Natural Language Processing (NLP) for Data Exploration In the vast world of Natural Language Processing, effective data exploration is a crucial step toward understanding and leveraging textual data. In this blog post, we’ll delve into three powerful techniques tailored for this purpose: data visualization, sentence similarity, and sentence clustering. To illustrate…
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A Deep Dive into Text Classification with TF-IDF
Introduction: Unlocking the potential within textual data is a rewarding journey, and text classification, a cornerstone of Natural Language Processing (NLP), stands as a beacon in this exploration. In this blog post, we delve into the intricacies of text classification using Python, Pandas, NLTK, and scikit-learn. Our practical example revolves around travel and food-related sentences,…
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Basics of Natural language processing & Part of Speech
Natural language processing (NLP) is an artificial intelligence area in which computers intelligently analyze, understand, and interpret from human language. Developers can use natural language processing (NLP) to organize and structure knowledge for tasks such automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. The Part of Speech tagging…