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added sentiment analysis using vader tutorial

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`@@ -35,6 +35,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy`
`35`	`35`	`- [How to Build a Spam Classifier using Keras in Python](https://www.thepythoncode.com/article/build-spam-classifier-keras-python). ([code](machine-learning/nlp/spam-classifier))`
`36`	`36`	`- [How to Build a Text Generator using TensorFlow and Keras in Python](https://www.thepythoncode.com/article/text-generation-keras-python). ([code](machine-learning/nlp/text-generator))`
`37`	`37`	`- [How to Perform Text Classification in Python using Tensorflow 2 and Keras](https://www.thepythoncode.com/article/text-classification-using-tensorflow-2-and-keras-in-python). ([code](machine-learning/nlp/text-classification))`
	`38`	`+- [Sentiment Analysis using Vader in Python](https://www.thepythoncode.com/article/vaderSentiment-tool-to-extract-sentimental-values-in-texts-using-python). ([code](machine-learning/nlp/sentiment-analysis-vader))`
`38`	`39`	`-###[Computer Vision](https://www.thepythoncode.com/topic/computer-vision)`
`39`	`40`	`-[How to Detect Human Faces in Python using OpenCV](https://www.thepythoncode.com/article/detect-faces-opencv-python). ([code](machine-learning/face_detection))`
`40`	`41`	`-[How to Make an Image Classifier in Python using TensorFlow and Keras](https://www.thepythoncode.com/article/image-classification-keras-python). ([code](machine-learning/image-classifier))`

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	`1`	`+#[Sentiment Analysis using Vader in Python](https://www.thepythoncode.com/article/vaderSentiment-tool-to-extract-sentimental-values-in-texts-using-python)`
	`2`	+-`pip3 install -r requirements.txt`

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	`1`	`+vaderSentiment`

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	`1`	`+fromvaderSentiment.vaderSentimentimportSentimentIntensityAnalyzer`
	`2`	`+`
	`3`	`+# init the sentiment analyzer`
	`4`	`+sia=SentimentIntensityAnalyzer()`
	`5`	`+`
	`6`	`+sentences= [`
	`7`	`+"This food is amazing and tasty !",`
	`8`	`+"Exoplanets are planets outside the solar system",`
	`9`	`+"This is sad to see such bad behavior"`
	`10`	`+]`
	`11`	`+`
	`12`	`+forsentenceinsentences:`
	`13`	`+score=sia.polarity_scores(sentence)["compound"]`
	`14`	`+print(f'The sentiment value of the sentence :"{sentence}" is :{score}')`
	`15`	`+`
	`16`	`+forsentenceinsentences:`
	`17`	`+print(f'For the sentence "{sentence}"')`
	`18`	`+polarity=sia.polarity_scores(sentence)`
	`19`	`+pos=polarity["pos"]`
	`20`	`+neu=polarity["neu"]`
	`21`	`+neg=polarity["neg"]`
	`22`	`+print(f'The percententage of positive sentiment in :"{sentence}" is :{round(pos*100,2)} %')`
	`23`	`+print(f'The percententage of neutral sentiment in :"{sentence}" is :{round(neu*100,2)} %')`
	`24`	`+print(f'The percententage of negative sentiment in :"{sentence}" is :{round(neg*100,2)} %')`
	`25`	`+print("="*50)`

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