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I have a collection of educational dataset. The dataset consists of a username and their review for the course. I want to analyze the data for sentiment analysis.

How can I label the data to train the model for my supervised machine learning model?

Do I need to label manually (Positive, negative, neutral) to train the model?

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If you have a smaller dataset, then I think manual labelling could work.

For larger datasets, you need to use a sentiment analyser engine which has been retained on a very large dataset.

For example, you can use TextBlob,

from textblob import TextBlob
obj = TextBlob( some_text )

print( obj.sentiment.polarity )

You can one by one load each review and sort them into three classes or continuous values.

  • You can classify the review into a categorical variable. For example, if the polarity is above 0 then the review is positive else it is negative. Here, you will perform classification.

  • Use the values from TextBlob as they are. Hence, they will form continuous labels. Here, you will perform regression.

Tip:

Also, you can search for similar packages which provide sentiment score for a text. You can use Natural Language Processing APIs.

import urllib

data = urllib.urlencode({"text": "I'm a very good boy "}) 
u = urllib.urlopen("http://text-processing.com/api/sentiment/", data )
the_page = u.read()

print (the_page)
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