Presidential Debate Twitter Sentiment Analysis using Python and NLTK
Imagine you’ve got customer review.
Working out whether it’s a good or bad review is pretty easy right?
You, read it, then you get a ‘feel’ for whether it’s good or bad.
Well, now imagine you have 500 review, or 5,000 or even 5 million.
Getting through all of these and working out which of them is REALLY bad or REALLY good is a whole lotta work. This is where sentiment analysis comes in. It allows you to leverage natural language processing to help speed up this process and work out whether something is good or bad. And because you’re able to do it using code…you can do it FAST. In this video you’ll get to do just that. You’ll learn how to apply sentiment analysis to the #PresidentialDebate Twitter feed in order to calculate overall sentiment (positive or negative) for each presidential candidate.
In this video you’ll learn how to:
1. Setting up Twitter Dev
2. Querying #PresidentialDebate tweets from Twitter using Python
3. Using NLTK and TextBlob to calculate sentiment
When using TextBlob for sentiment analysis, you’re able to extract polarity and subjectivity. Polarity refers to how positive or negative something is with the range extending from -1 (negative) to 1 (positive). Subjectivity refers to how much a piece of text is based on emotion with 0 being the least subjective and 1 being the most subjective.
Sentiment Analysis can also (and is typically) used for:
* Market research
* Customer feedback
* Financial markets analysis
Note: All content, ideas and opinions are my own!
GitHub:
https://github.com/nicknochnack/TwitterPresidentialDebate
Resources Listed:
Twitter Developer: http://developer.twitter.com/en/apps
Twepy: https://www.tweepy.org/
NLTK: https://www.nltk.org/
Pandas: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html
Oh, and don't forget to connect with me!
LinkedIn: https://www.linkedin.com/in/nicholasr...
Facebook: https://www.facebook.com/nickrenotte/
GitHub: https://github.com/nicknochnack
Happy coding!
Nick
P.s. Let me know how you go and drop a comment if you need a hand!
Technology vector created by freepik - www.freepik.com
Видео Presidential Debate Twitter Sentiment Analysis using Python and NLTK канала Nicholas Renotte
Working out whether it’s a good or bad review is pretty easy right?
You, read it, then you get a ‘feel’ for whether it’s good or bad.
Well, now imagine you have 500 review, or 5,000 or even 5 million.
Getting through all of these and working out which of them is REALLY bad or REALLY good is a whole lotta work. This is where sentiment analysis comes in. It allows you to leverage natural language processing to help speed up this process and work out whether something is good or bad. And because you’re able to do it using code…you can do it FAST. In this video you’ll get to do just that. You’ll learn how to apply sentiment analysis to the #PresidentialDebate Twitter feed in order to calculate overall sentiment (positive or negative) for each presidential candidate.
In this video you’ll learn how to:
1. Setting up Twitter Dev
2. Querying #PresidentialDebate tweets from Twitter using Python
3. Using NLTK and TextBlob to calculate sentiment
When using TextBlob for sentiment analysis, you’re able to extract polarity and subjectivity. Polarity refers to how positive or negative something is with the range extending from -1 (negative) to 1 (positive). Subjectivity refers to how much a piece of text is based on emotion with 0 being the least subjective and 1 being the most subjective.
Sentiment Analysis can also (and is typically) used for:
* Market research
* Customer feedback
* Financial markets analysis
Note: All content, ideas and opinions are my own!
GitHub:
https://github.com/nicknochnack/TwitterPresidentialDebate
Resources Listed:
Twitter Developer: http://developer.twitter.com/en/apps
Twepy: https://www.tweepy.org/
NLTK: https://www.nltk.org/
Pandas: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html
Oh, and don't forget to connect with me!
LinkedIn: https://www.linkedin.com/in/nicholasr...
Facebook: https://www.facebook.com/nickrenotte/
GitHub: https://github.com/nicknochnack
Happy coding!
Nick
P.s. Let me know how you go and drop a comment if you need a hand!
Technology vector created by freepik - www.freepik.com
Видео Presidential Debate Twitter Sentiment Analysis using Python and NLTK канала Nicholas Renotte
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Sentiment Analysis with BERT Neural Network and PythonTwitter Sentiment Analysis Using PythonSean Law - Modern Time Series Analysis with STUMPY - Intro To Matrix Profiles | PyData Global 2020Automatic Summarization using Deep Learning | Abstractive Summarization with PegasusHow I taught myself to code | Litha Soyizwapi | TEDxSowetoReal Time Sign Language Detection with Tensorflow Object Detection and Python | Deep Learning SSDHow to get TWITTER data and analyze it using Python [official API]You Should Be Using Automatic DifferentiationSentiment Analysis Using Machine Learning and Python | Sentiment Analysis | NLP Tutorials in HindiSentiment Analysis on News Articles for Cryptocurrencies With PythonAnalyzing Twitter Accounts with Python and Personality InsightsNLP: Twitter Sentiment Analysis | Coursera Guided ProjectSentiment Analysis Project using Machine Learning NLP | Review Classification | ML EducationTweet Visualization and Sentiment Analysis in Python - Full TutorialHow to Read, View and Export Images using OpenCV for Python // OpenCV for BeginnersMedia Monitoring Masterclass 🎓 | Social Media Listening and Online Reputation Management ExplainedA Quick Guide To Sentiment Analysis | Sentiment Analysis In Python Using Textblob | Edureka[Python Project] Sentiment Analysis and Visualization of Stock NewsNLP for Beginners - Sentiment Analysis of Twitter Data Using Scikit-Learn in Python