## Dictionary Method: Exercise Solutions

First I'll recreate what we did in the tutorial.

``````

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#import the necessary packages
import pandas
import nltk
from nltk import word_tokenize
import string

#read the Music Reviews corpus into a Pandas dataframe
df = pandas.read_csv("../Data/BDHSI2016_music_reviews.csv", encoding='utf-8', sep = '\t')
df['body'] = df['body'].apply(lambda x: ''.join([i for i in x if not i.isdigit()]))
df['body_tokens'] = df['body'].str.lower()
df['body_tokens'] = df['body_tokens'].apply(nltk.word_tokenize)
df['body_tokens'] = df['body_tokens'].apply(lambda x: [word for word in x if word not in string.punctuation])
df['token_count'] = df['body_tokens'].apply(lambda x: len(x))

#view the dataframe
df

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#view part of the pos_sent variable, to see how it's formatted.
print(pos_sent[:101])

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#remember the split function? We'll split on the newline character (\n) to create a list
positive_words=pos_sent.split('\n')
negative_words=neg_sent.split('\n')

#view the first elements in the lists
print(positive_words[:10])
print(negative_words[:10])

``````

Great! You know what to do now.

Exercise:

1. Create a column with the number of positive words, and another with the proportion of positive words
2. Create a column with the number of negative words, and another with the proportion of negative words
3. Print the average proportion of negative and positive words by genre
4. Compare this to the average score by genre
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#exercise code here
#1. Create a column with the number of positive words and another with the proportion of positive words
df['pos_num'] = df['body_tokens'].apply(lambda x: len([word for word in x if word in positive_words]))
df['pos_prop'] = df['pos_num']/df['token_count']

#2. Create a column with the number of negative words, and another with the proportion of negative words
df['neg_num'] = df['body_tokens'].apply(lambda x: len([word for word in x if word in negative_words]))
df['neg_prop'] = df['neg_num']/df['token_count']

df

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#3. Print the average proportion of negative and positive words by genre
grouped = df.groupby('genre')
print("Averge proportion of positive words by genre")
print(grouped['pos_prop'].mean().sort_values(ascending=False))
print()
print("Averge proportion of negative words by genre")
grouped['neg_prop'].mean().sort_values(ascending=False)

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# 4. Compare this to the average score by genre
print("Averge score by genre")
grouped['score'].mean().sort_values(ascending=False)

``````

### 3. Dictionary Method using Scikit-learn

We can also do this using the document term matrix. We'll again do this in pandas, to make it conceptually clear. As you get more comfortable with programming you may want to eventually shift over to working with sparse matrix format.

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#import the function CountVectorizer
from sklearn.feature_extraction.text import CountVectorizer

countvec = CountVectorizer()

#create our document term matrix as a pandas dataframe
dtm_df = pandas.DataFrame(countvec.fit_transform(df.body).toarray(), columns=countvec.get_feature_names(), index = df.index)

``````

Now we can keep only those columns that occur in our positive words list. To do this, we'll first save a list of the columns names as a variable, and then only keep the elements of the list that occur in our positive words list. We'll then create a new dataframe keeping only those select columns.

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#create a columns variable that is a list of all column names
columns = list(dtm_df)
pos_columns = [word for word in columns if word in positive_words]

#create a dtm from our dtm_df that keeps only positive sentiment columns
dtm_pos = dtm_df[pos_columns]

#count the number of positive words for each document
dtm_pos['pos_count'] = dtm_pos.sum(axis=1)
#dtm_pos.drop('pos_count',axis=1, inplace=True)
dtm_pos['pos_count']

``````

EX: Do the same for negative words.
EX: Calculate the proportion of negative and positive words for each document.

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#EX: Do the same for negative words.

neg_columns = [word for word in columns if word in negative_words]
dtm_neg = dtm_df[neg_columns]

dtm_neg['neg_count'] = dtm_neg.sum(axis=1)
dtm_neg['neg_count']

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#EX: Calculate the proportion of negative and positive words for each document.

dtm_pos['pos_proportion'] = dtm_pos['pos_count']/dtm_df.sum(axis=1)
print(dtm_pos['pos_proportion'])
print()
dtm_neg['neg_proportion'] = dtm_neg['neg_count']/dtm_df.sum(axis=1)
print(dtm_neg['neg_proportion'])

``````