Labeling an Unlabelled NLP Dataset(s) using Different Methods

A recent predicament I have crossed recently is the lack of suitable datasets or corpus to train your model on. Sometimes even when having sufficient data if it’s not labeled then it does little benefit

Introduction:

A recent predicament I have crossed recently is the lack of suitable datasets or corpus to train your model on. Sometimes even when having sufficient data if it’s not labeled then it does little benefit. Usually, the go to solution for something like this is unsupervised learning in order to cluster or group them into classes, thus creating the dependent column one wishes for. In this article, I will attempt to label an unlabelled dataset using two methods I have experimented with before which are: A python library called Textblob and a pickled model.

Using TextBlob:

What is TextBlob you ask? TextBlob is a Python library made for NLP data and preprocessing and has many common features with something like NLTK such as bag-of-words, tokenization, and the one we’re using today: sentiment analysis. If you have heard of Python Vader, then think of it as really similar-minded libraries, if not here is a link.

In order to download and use TextBlob, just follow this link.

Now that we have installed TextBlob successfully the next step is to simply start our exercise. First what we need is to import the libraries needed to use. We will import the pandas library to import and tabulate the data, next is the Textblob library and the “NaiveBayesAnalyzer” from Textblob as well.

import pandas as pd
from textblob import TextBlob
from textblob.sentiments import NaiveBayesAnalyzer

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