Understanding Data for Sentiment Analysis

Sentiment analysis is a type of text classification. Sentiment analysis models are usually trained using supervised datasets. Supervised datasets are a kind of dataset that are labeled with the target variable – usually a column that specifies the sentiment value in the text. This is the value we want to predict in the unseen text.

Exercise 64: Loading Data for Sentiment Analysis

In this exercise, we will load data that could be used to train a sentiment analysis model. For this exercise, we will be using three datasets, namely Amazon, Yelp, and IMDB. Follow these steps to implement this exercise:

1. Open a Jupyter notebook.
2. Insert a new cell and add the following code to import the necessary libraries:

   import pandas as pd
   pd.set_option('display.max_colwidth', 200)

   This imports the pandas library. It also sets the display width to 200 characters so that more of the review text is displayed on the screen.

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