def remove_stop_words(column):
'''gets rid of any words that are on the stop word list'''
cleaned_responses = []
for response in column:
cleaned_response = TextBlob('').words
for word in response:
if word not in stop_words:
cleaned_response.append(word)
cleaned_responses.append(cleaned_response)
return cleaned_responses
def column_lemmatize(column):
'''Lemmatizes down an entire column'''
lemmatized_goals = []
for response in column.apply(lambda goal: goal.tags):
lemmatized_words = TextBlob('').words
for word_and_tag in response:
corrected_tag = _penn_to_wordnet(word_and_tag[1])
lemmatized_words.append(word_and_tag[0].lemmatize(corrected_tag))
lemmatized_goals.append(lemmatized_words)
return lemmatized_goals
score.sentiment
import pandas as pd
df = pd.DataFrame()
tweets = []
for i in public_tweets:
tweets.append(i.text)
df['Tweets'] = tweets
df
sentiment = []
for i in public_tweets:
s = (TextBlob(i.text)).sentiment
sentiment.append(s)
df['Sentiments'] = sentiment
df
score = []
for i in public_tweets:
s = (TextBlob(i.text)).sentiment
if s[0] > 0:
score.append("Postive")
elif s[0] < 0:
score.append("Negative")
else:
score.append("Neutral")
df['Score'] = score
df
import pandas as pd
data = pd.DataFrame()
# Extract the 15 tweets
tweets = []
for i in public_tweets:
tweets.append(i.text)
data['Tweets'] = tweets
print(data)
# Calculating the score for 15 tweets
score = []
for i in public_tweets:
s = (TextBlob(i.text)).sentiment
score.append(s)
data['Sentiments'] = score
print(data)
metrics = []
for i in public_tweets:
s = (TextBlob(i.text)).sentiment
if s[0] > 0:
metrics.append("Positive")
elif s[0] < 0:
metrics.append("Negative")
else:
metrics.append("Neutral")
data['Metric'] = metrics
print(data)
