Contents
Can machine learning analyze your tweets?
Create a sentiment analysis machine learning model. Analyze your Twitter data using your sentiment analysis model. Visualize the results of your Twitter sentiment analysis….3. Create a Twitter Sentiment Analysis Model
- Choose a model type.
- Decide which type of classification you’d like to do.
- Import your Twitter data.
How do you analyze on Twitter?
To access your Tweet activity: On a desktop or laptop computer, visit analytics.twitter.com and click on Tweets. In the Twitter app for iOS or Android, tap the analytics icon visible in your Tweets.
How to use machine learning in Twitter topic modeling?
Using Machine Learning ( Gensim Linear Discriminant Analysis — LDA) to explore what topics your followers are engaging with the most. I’m a machine learning geek and I’d like to apply machine learning on everything that I can just to see the results.
How to classify tweets based on Naive Bayes?
Get a list of words in the training data. Get the probability of each word in the training data of tweet. Get the probability of a given label. Apply the Naive Bayes Model like below. Print the labeled or categorize the test data like below. Read the training and test data like below. Get the probability of each word.
How is lemmatization used in Twitter topic modeling?
As a result, the number of columns in the document-word matrix will be denser with lesser columns. The ultimate goal of lemmatization is to help the LDA model to produce better topics in the end. We produce a base model first to be used to track our progress as we go through the hyper-parameter tuning stage.
How to create a topic model for Twitter?
After pulling and refining the data from the Twitter api and importing the required packages, we had to clean the data from emojis and urls so we can tokenize it for the next steps. You can find the cleaning syntaxes we used in the below snippet: