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.

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.