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@ -209,18 +209,18 @@ Steps:
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4. Using the word counter, show the 15 most used tokenized words in the datasets' tweets |
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5. Add to your `count_vecotrized_df` a `label` column considering the following: |
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- 1: Positive |
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- 0: Neutral |
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- -1: Negative |
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The final DataFrame should be similar to the below: |
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| | ... | label | |
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|---:|-------:|--------:| |
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| 0 | ... | 1 | |
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| 1 | ... | -1 | |
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| 2 | ... | -1 | |
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| 3 | ... | -1 | |
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| | ... | label | |
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| --: | --: | ----: | |
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| 0 | ... | 1 | |
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| 1 | ... | -1 | |
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| 2 | ... | -1 | |
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| 3 | ... | -1 | |
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_Resources: [sklearn.feature_extraction.text.CountVectorizer](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html)_ |
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