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Python 3 Sentiment Text Analysis Project Using Machine Learning Models & NLTK ChatGPT Library
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Jun 3, 2025
Get the full source code of application here: https://codingshiksha.com/python/python-3-sentiment-text-analysis-project-using-machine-learning-models-nltk-chatgpt-library/
View Video Transcript
0:00
uh hello guys welcome to this video So
0:02
in this video I will show you how to do
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sentimental uh analysis of any sort of
0:08
sentence
0:10
uh in English language using uh uh
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machine learning models and uh national
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uh natural language processing chart GPT
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library So inside Python we can actually
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do sentimental analysis of sentences
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that whether a sentence is positive
0:29
negative neutral what is the tone of the
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sentence So we have this bas basically
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this uh Python script here We have a
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series of sentences that I have written
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right here Uh so you will see it will
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exactly tell you the tone of the
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sentence So we have three sentences
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right here I know in my heart nobody
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comes close to me and I am the greatest
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player in the world So this is actually
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a positive kind of a sentence Let me
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first of all show you if I execute this
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Python script what is the output that
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I'm getting So first of all you need to
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install this module here which is Vader
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sentimental So the command is simple You
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simply say pip install uh
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vader
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sentiment So you simply install this
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module I have
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uh you just need to execute this command
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So this will hardly take 5 to 10 seconds
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and it will install this and after that
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you just need to execute this command
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here and exactly it will tell you the
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sentiment and analysis of each sentence
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it will give you a score based upon the
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tone of the sentence So you can see the
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first statement has a negative sentiment
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of uh 0% and a neutral sentiment of
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65.6% and a positive sentiment of
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34.4% So overall sentiment is positive
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The second statement that I have taken
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this example if you see Shaa played well
2:02
in the match as usual So this is a again
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a positive kind of
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a sentiment and it has given me the
2:11
positive rating which is
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42.9% And the third sentence is negative
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as you can see negative sentiment is 40%
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and neutral is 39% And the sentence was
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if you see I am feeling sad today So
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this is actually a kind of a negative
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kind of a sentence So in this easy way
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using this free module of Python you can
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actually do the
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sentiment
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analysis So the module is very simple
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You simply install this module pip
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install Vader
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sentiment So install this It uses
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machine learning models and
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NLTKJ chart GPT library in the
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background I've given all the source
2:57
code in the description of the video So
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now I will show you just create a simple
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Python file and then from Vader
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sentiment you just need to import Vader
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sentiment and from this you need to
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import sentiment intensity
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analyzer After
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that we will define a simple function
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which will give the sentiment scores
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based upon the
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sentence given as an
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argument So here we will
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basically create an object here
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initializing the sentiment and intensity
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analyzer After
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that we will basically make a dictionary
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based upon
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the
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sid and it contains this function here
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which
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is
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polarity scores So it will give you the
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scores here based upon the sentence
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which is given
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And then you can print out the scores
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here
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So we are printing out this dictionary
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based upon it contains three kind of
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score negative neutral and
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positive And uh now we just need to call
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this function So we will basically take
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three examples of
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sentences So I will call this function
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inside the main function So right here
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we are providing three sentences again
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you will see I know in my heart nobody
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comes close and I'm the greatest player
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in the world I am feeling sad today and
5:01
Shwa played well
5:04
in so if you run this script
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here it is saying underscore object is
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not
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defined set object
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set unis
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code I've given all this
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uh source code in the description of the
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video guys So if you need the full
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source code the link is given in the
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description
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So so now I fixed this It's just a typo
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mistake And if I refresh it you will see
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that it will give you these three
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scores Percentages negative sentiment
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neutral positive the percentages it will
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give you the sentiment analysis of each
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sentence So in this easy way you can use
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this free module inside Python to
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actually do the sentiment analysis of
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each sentence You can basically give any
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sentence of English language and based
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upon that it will give you these
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percentages of neutral positive negative
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like this So thank you very much for
6:28
watching this video and also check out
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my website freemediattools.com
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uh which contains thousands of tools