Working with text is generally more challenging than working with numerical data. Hence, any kind of technique that helps in generating an intuition of the existing dataset is welcome. One of the simplest approach to understand any text document or to compare multiple documents can be to compute a frequency table of individual words present in the document/documents and use it to conduct further experiements like: finding top words per document, finding top common words among documents etc.
In our case, we have taken the challenge of Analyzing Sentiments from Twitter data, so we will focus on how to generate word frequencies and use it to create Word Clouds in Python that will help us get a better overall understanding of the dataset.
EDA using Word Clouds After succesfully creating a frequency table, you can use that to create multiple visualizations in the form of word clouds. Sometimes, the quickest way to understand the context of the text data is using a word cloud of top 100-200 words.
pip install wordcloud