Sentiment Analysis Sentiment analysis involves determining the sentiment of text
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Sentiment Analysis
Sentiment analysis involves determining the sentiment of text.
In this lab, you will use a hotel review data set that includes reviews and a rating There are other features that you can ignore, unless you want to use them to improve results
Your goal is to train a model that can predict the number of stars based on the text
This is the last programming assignment. We will use similar cleaning and discovery techniques as other assignments… except we need to add the fun of stop words, stemming / lemmatizing and similar exciting topics.
Dont forget to save this as a copy in your Google Colab environment
Link for google collab https://colab.research.google.com/drive/18tUoWk0Ba…
Get the data
Either download the data and store it in your drive or use the Kaggle API to obtain the data fromhttps://www.kaggle.com/datasets/datafiniti/hotel-reviews
Explore and Clean the Data
Double-click (or enter) to edit
Train the Model
Train the model using 90% of the data
You may choose whichever model technique you choose
Test the Model
Test the model using the remaining 10% of the data
The testing results will depend on the model you useIf the rating is evaluated as a number, you need to look at values such as mean square error
If you are using categories, then you can use accuracy, but you may
want to collapse the categories from 1 to 5 to 3 categories such as bad,
neutral, and good.
Provide an explanation of your model and results
Your explanation here
DO NOT FORGET TO DO THE ANALYSIS PART
Discuss techniques you could use to improve your model if you had more time
Your explanation here
