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Text Mining with Python (week 9)

Authored by Mikhail Bukhtoyarov

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Text Mining with Python (week 9)
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7 questions

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1.

MULTIPLE SELECT QUESTION

30 sec • 1 pt

Natural language processing (NLP) is a subfield of ... and ... that uses machine learning to enable computers to understand and communicate with human language.

corpus linguistics

computer science

statistics

2.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

What is the correct order of preprocessing the text for further mining?

tokenization, lemmatization, and removing stop words

lemmatization, tokenization, and removing stop words

lemmatization, removing stop words, and tokenization

removing stop words, lemmatization, and tokenization

3.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

What does this piece of code do?

def most_common(df, top_n=10):
all_words = [word for word in ' '.join(df['processed_comments']).split() if word not in string.punctuation]
word_counts = Counter(all_words)
top_10_words = word_counts.most_common(top_n)
return top_10_words

identifies the most recurrent themes or issues

tokenizes the textual data

categorizes comments

scrapes the html files to csv

4.

MULTIPLE SELECT QUESTION

1 min • 1 pt

What Python libraries are used for text mining?

NLTK

Pandas

BeutifulSoup

TextBlob

5.

MULTIPLE SELECT QUESTION

1 min • 1 pt

What can NLTK (natural language toolkit) be used for?

stemming and lemmatization

tokenization

sentiment analysis

speech recognition

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is Pandas library used for?

working with MS Office files

building neural networks

data manipulation and analysis

speech recognition

7.

MULTIPLE CHOICE QUESTION

45 sec • 1 pt

The goal of sentiment analysis is to determine the overall sentiment polarity of a piece of text, which can be ...

positive, negative, or neutral

positive, negative, emotional, or neutral

sentimental, rational

poetical, prosaic, academic, legal, etc.

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