Category: Text

Text

Used to format docstrings in Python files or reStructuredText

Style-Doc Style-Doc is Black for Python docstrings and reStructuredText (rst). It can be used to format docstrings (Google docstring format) in Python files or reStructuredText. Installation Style-Doc is available at the Python Package Index (PyPI). It can be installed with pip: $ pip install style-doc Usage $ style-doc –help usage: style-doc [-h] [–max_len MAX_LEN] [–check_only] [–py_only] [–rst_only] files [files …] positional arguments: files The file(s) or folder(s) to restyle. optional arguments: -h, –help show this help message and exit –max_len […]

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Advanced, NLP, Project, Python, Text

Part 3: Step by Step Guide to NLP – Text Cleaning and Preprocessing

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). In part-1and  part-2 of this blog series, we complete the theoretical concepts related to NLP. Now, in continuation of that part, in this article, we will cover some of the new concepts. In this article, we will understand the terminologies required and then we start our journey towards text cleaning and preprocessing, which is […]

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Beginner, Data Cleaning, NLP, Python, Text, Unstructured Data, Word Embeddings

Part 6: Step by Step Guide to Master NLP – Word2Vec

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). In the previous article of this series, we completed the statistical or frequency-based word embedding techniques, which are pre-word embedding era techniques. So, in this article, we will discuss the recent word-era embedding techniques. NOTE: In recent word-era embedding, there are many such techniques but in this article, we will discuss only the Word2Vec […]

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Advanced, NLP, Python, Text, Unstructured Data

Part 13: Step by Step Guide to Master NLP – Regular Expressions

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). From this article, we will start our discussion on Regular Expressions. When a data scientist comes across a text processing problem whether it is searching for titles in names or dates of birth in a dataset, regular expressions rear their ugly head very frequently. They form part of the basic techniques in NLP and […]

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Data Science, Libraries, NLP, Python, Text, Unstructured Data

Part 2: Topic Modeling and Latent Dirichlet Allocation (LDA) using Gensim and Sklearn

This article was published as a part of the Data Science Blogathon Introduction In the previous article, we had started with understanding the basic terminologies of text in Natural Language Processing(NLP), what is topic modeling, its applications, the types of models, and the different topic modeling techniques available. Let’s continue from there, explore Latent Dirichlet Allocation (LDA), working of LDA, and its similarity to another very popular dimensionality reduction technique called Principal Component Analysis (PCA).   Table of Contents A Little […]

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Beginner, Libraries, NLP, Python, Text, Topic Modeling

Topic modeling With Naive Bayes Classifier

This article was published as a part of the Data Science Blogathon Introduction Naive Bayes is a powerful tool that leverages Bayes’ Theorem to understand and mimic complex data structures. In recent years, it has commonly been used for Natural Language Processing (NLP) tasks, such as text categorization. Today, we will be constructing a Naive Bayes text classifier for topic categorization. Before we move forward with the explanation, I want to emphasize that Naive Bayes is not the traditional method of […]

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Advanced, NLP, Project, Python, Text, Unstructured Data

Part 2: Step by Step Guide to NLP – Knowledge Required to Learn NLP

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). In part-1 of this blog series, we complete the basic concepts of NLP. Now, in continuation of that part, in this article, we will cover some of the new concepts. In this article, we will understand the knowledge required and levels of NLP in a detailed manner. In the last of this article, we […]

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Beginner, Data Cleaning, Machine Learning, NLP, Python, Text, Word Embeddings

Part 5: Step by Step Guide to Master NLP – Word Embedding and Text Vectorization

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). Up to the previous part of this article series, we almost completed the necessary steps involved in text cleaning and normalization pre-processing. After that, we will convert the processed text to numeric feature vectors so that we can feed it to computers for Machine Learning applications. NOTE: Some concepts included in the pipeline of […]

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Advanced, Classification, Libraries, NLP, Project, Python, Structured Data, Supervised, Text

Sentiment Analysis using NLTK – A Practical Approach

This article was published as a part of the Data Science Blogathon Introduction The ultimate goal of this blog is to predict the sentiment of a given text using python where we use NLTK aka Natural Language Processing Toolkit, a package in python made especially for text-based analysis. So with a few lines of code, we can easily predict whether a sentence or a review(used in the blog) is a positive or a negative review. Before moving on to the implementation […]

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Classification, News, NLP, Project, Python, Text

Build Your Own Fake News Classifier With NLP

img src: https://wallpapercave.com/w/wp7461543 Introduction The major objective of watching or reading news was to be informed about whatever is happening around us. There are several social media platforms in the current modern era, like Facebook, Twitter, Reddit, and so forth where millions of users would rely upon for knowing day-to-day happenings. Then came the fake news which spread across people as fast as the real news could. Fake news is a piece of incorporated or falsified information often aimed at misleading […]

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