From ab205d6bfafa395988ddbbbcad859ed72ef12d4e Mon Sep 17 00:00:00 2001 From: nprimo Date: Thu, 14 Mar 2024 12:06:28 +0000 Subject: [PATCH] chore(nlp): run prettier --- subjects/ai/nlp/README.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/subjects/ai/nlp/README.md b/subjects/ai/nlp/README.md index 43b14f971..49ce33bb1 100644 --- a/subjects/ai/nlp/README.md +++ b/subjects/ai/nlp/README.md @@ -204,23 +204,23 @@ Steps: > Note: The sample 3x3 table mentioned is a small representation of the expected output for demonstration purposes. It's not necessary to drop columns in this context. -3. Show the token counts (obtained with the above-mentioned steps) of the fourth tweet. +3. Show the token counts (obtained with the above-mentioned steps) of the fourth tweet. -4. Using the word counter, show the 15 most used tokenized words in the datasets' tweets +4. Using the word counter, show the 15 most used tokenized words in the datasets' tweets 5. Add to your `count_vecotrized_df` a `label` column considering the following: + - 1: Positive - 0: Neutral - -1: Negative The final DataFrame should be similar to the below: - -| | ... | label | -|---:|-------:|--------:| -| 0 | ... | 1 | -| 1 | ... | -1 | -| 2 | ... | -1 | -| 3 | ... | -1 | +| | ... | label | +| --: | --: | ----: | +| 0 | ... | 1 | +| 1 | ... | -1 | +| 2 | ... | -1 | +| 3 | ... | -1 | _Resources: [sklearn.feature_extraction.text.CountVectorizer](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html)_