Sentiment Classification

We finally made it to the final chapter of this project! Now that we know how to understand the sentiment of a tweet we will iterate over our DataFrame and append the results in a new column called "sentiment_predicted".

Methods description

for index, row in data.iterrows():: Iterates through the rows of the DataFrame data;
blob = TextBlob(row["text"]): Creates a TextBlob object for each text in the "text" column of the DataFrame;
sentiment = blob.sentiment.polarity: Calculates the sentiment polarity of the text using the sentiment attribute of the TextBlob object. Polarity is a float value within the range [-1.0, 1.0], where negative values indicate negative sentiment, positive values indicate positive sentiment, and zero indicates neutral sentiment;
data.at[index, "sentiment_predicted"] = sentiment: Assigns the calculated sentiment polarity to the "sentiment_predicted" column of the corresponding row in the DataFrame data using the at method.

Task

Create a new empty column called "sentiment_predicted".
Use the .iterrows() method to iterate over the DataFrame.
Append the result of the polarity function for a specific index.

Great job on completing your course on classifying sentiments of tweets! Understanding the sentiment behind tweets can be a valuable tool in many industries. This knowledge can help in areas like marketing, customer service, and even politics. I hope you have enjoyed the course and feel equipped with a new set of skills. Keep up the good work, I'm sure you will excel in your future endeavors. Remember to keep practicing and stay up-to-date with new trends and technologies in the field.

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Methods description

for index, row in data.iterrows():: Iterates through the rows of the DataFrame data;
blob = TextBlob(row["text"]): Creates a TextBlob object for each text in the "text" column of the DataFrame;
sentiment = blob.sentiment.polarity: Calculates the sentiment polarity of the text using the sentiment attribute of the TextBlob object. Polarity is a float value within the range [-1.0, 1.0], where negative values indicate negative sentiment, positive values indicate positive sentiment, and zero indicates neutral sentiment;
data.at[index, "sentiment_predicted"] = sentiment: Assigns the calculated sentiment polarity to the "sentiment_predicted" column of the corresponding row in the DataFrame data using the at method.

Task

Create a new empty column called "sentiment_predicted".
Use the .iterrows() method to iterate over the DataFrame.
Append the result of the polarity function for a specific index.

Mark tasks as Completed

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 1. Chapter 11

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