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Exploratory Data Analysis (EDA) on Text Data with Python
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Exploratory Data Analysis (EDA) on Text Data with Python

Last Updated on January 5, 2024 by Editorial Team

Author(s): Davide Nardini

Originally published on Towards AI.

Exploratory Data Analysis (EDA) is the first step of a Machine Learning Project on Text Data with Python code and snippets.
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Exploratory Data Analysis (EDA) represents the initial step in a Machine Learning Project. In Data Science, EDA involves analyzing data to identify their primary characteristics and information.

EDA often utilizes Data Visualization techniques to summarize dataset information and employs basic or more complex statistical measures such as mean, median, standard deviation, or data distribution.

When dealing with Text Data, the foundational approach remains similar, yet the tools employed may differ.

Consider a dataset with columns for Hotel Review and Rating. Can we employ the standard EDA approach here? Yes, but it might result in a limited analysis. For instance, describing the Rating variable using mean and median might not provide substantial insights. With text data, our focus shifts to other tools such as word counting, word frequency, and bigrams.

Let’s delve into the Exploratory Data Analysis of Text Data.

Import of Python Package for EdaLoad data and first overviewPreprocessingLength and Word CountFrequency WordsFrequency Bigrams for Exploratory Data Analysis on TextFinal Thoughts on Exploratory Data Analysis on Text Data

For this tutorial, we’ll employ the primary Python packages for conducting Exploratory Data Analysis on Text Data.

We’ll utilize Pandas to handle datasets (referred to as DataFrame in Pandas), Matplotlib and Seaborn for Data Visualization, re for… Read the full blog for free on Medium.

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