This is amazing, well structured and right to the point in the explanation, thanks. I am really interested in Text mining and Text analytics, please I would love to see more about it.
Thank you for this video! I have a question: after setting the stopwords and looking at the filtered sentence (19:53) : why is the filtered sentence equal the tokenized sentence when the stopword list includes e.g. doing? Shouldn't it be deleted from the filtered sentence? An explaination would help me a lot. Thank you!
I've been checking what I have this type of error. Hope you can help. TypeError Traceback (most recent call last) in () 6 7 word = "Working" ----> 8 print("Lemmatized Word: ", lem.lemmatize(word, "v")) 9 print("Stemmed Word: ", stem.stem(word)) 10 TypeError: 'tuple' object is not callable
This is amazing, well structured and right to the point in the explanation, thanks. I am really interested in Text mining and Text analytics, please I would love to see more about it.
Thanks for this wonderful video
Thank you for this video! I have a question: after setting the stopwords and looking at the filtered sentence (19:53) : why is the filtered sentence equal the tokenized sentence when the stopword list includes e.g. doing? Shouldn't it be deleted from the filtered sentence? An explaination would help me a lot. Thank you!
In the code tokenized-text should be replaced with tokenized_word. Then all stopwords can be removed.
@@yasinortakc6170 thank you!
I've been checking what I have this type of error. Hope you can help.
TypeError Traceback (most recent call last)
in ()
6
7 word = "Working"
----> 8 print("Lemmatized Word: ", lem.lemmatize(word, "v"))
9 print("Stemmed Word: ", stem.stem(word))
10
TypeError: 'tuple' object is not callable
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