From the course: Develop ML Models with Python and T-SQL

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Demo: Loading data for sentiment analysis

Demo: Loading data for sentiment analysis

From the course: Develop ML Models with Python and T-SQL

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Demo: Loading data for sentiment analysis

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- [Instructor] In the first part of demo three, we're going to load text data and use it for sentiment analysis. This data is found in a text file called Yelp_Reviews.txt. Essentially, it contains 991 different records containing two separate columns that are tab delimited. We have the actual review followed by the sentiment, and this repeats for all 991 records all throughout the file. So let's get started by importing it. Looking at our script, we're going to utilize the pyodbc and the PANDAS modules. We're going to specify the path of the file. Then we're going to use the pandas module to be able to read that text file. Although it's text, we're going to utilize the read_CSV method, and we're going to specify the path of the file, how it's delimited. In this case, it's using the tab delimiter, and we're going to assign names to the separate columns: review and sentiment. We establish a connection to the database, instantiate our cursor object, and like we've done before, we check…

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