Preparing data for sentiment analysis
Before diving into sentiment analysis, it’s crucial to prepare your data effectively. Data preparation is a process that involves cleaning, structuring, and enhancing data to improve analysis outcomes. The goal of these steps is to ensure that the data is in a form that is directly usable for analysis and to remove any inaccuracies or irregularities.
Let’s begin by loading the Twitter Airline Sentiment dataset:
import pandas as pd
df = pd.read_csv('Tweets.csv')
df.head(5)
Using df.columns, we can see a number of columns, such as text, which contains the tweet itself, along with several valuable metadata and sentiment-related fields. The following is a summary of the columns, along with a short description of their meaning:
|
Column |
Description |
|
|
... |
