def findBasics(name):
linesList = cleanText(name + '.txt')
#Check number of words
wc=0
for line in linesList:
wc=wc+len(str(line).split(" "))
print("Word count=",wc)
def analyze(name):
linesList = cleanText(name + '.txt')
neutral, negative, positive = 0, 0, 0
for index, sentence in enumerate(linesList):
print("Processing {0}%".format(str((index * 100) / len(linesList))))
# Ignore Emoji
if re.match(r'^[\w]', sentence):
continue
scores = sentiment_analyzer.polarity_scores(sentence)
# We don't need that component
scores.pop('compound', None)
maxAttribute = max(scores, key=lambda k: scores[k])
if maxAttribute == "neu":
neutral += 1
elif maxAttribute == "neg":
negative += 1
else:
positive += 1
total = neutral + negative + positive
print("Negative: {0}% | Neutral: {1}% | Positive: {2}%".format(
negative*100/total, neutral*100/total, positive*100/total))
labels = 'Neutral', 'Negative', 'Positive'
sizes = [neutral, negative, positive]
colors = ['#66c5f4', '#f47469', '#8cf442']
# Plot
plt.pie(sizes, labels=labels, colors=colors,
autopct='%1.1f%%', startangle=140)
plt.axis('equal')
plt.title("Sentiment Analysis")
plt.show()
def analyze(name):
linesList = cleanText(name)
neutral, negative, positive = 0, 0, 0
for index, sentence in enumerate(linesList):
# print("Processing {0}%".format(str((index * 100) / len(linesList))))
if re.match(r'^[\w]', sentence) is None:
continue
scores = sentiment_analyzer.polarity_scores(sentence)
scores.pop('compound', None)
maxAttribute = max(scores, key=lambda k: scores[k])
if maxAttribute == "neu":
neutral += 1
elif maxAttribute == "neg":
negative += 1
else:
positive += 1
total = neutral + negative + positive
print("Negative: {0}% | Neutral: {1}% | Positive: {2}%".format(
negative * 100 / total, neutral * 100 / total, positive * 100 / total))
labels = 'Neutral', 'Negative', 'Positive'
sizes = [neutral, negative, positive]
colors = ['#00bcd7', '#F57C00', '#CDDC39']
# Plot
plt.pie(sizes,
labels=labels,
colors=colors,
autopct='%1.1f%%',
startangle=140)
plt.axis('equal')
plt.title("Sentiment Analysis - Chat with {0}".format(name.capitalize()))
plt.show()
def analyze(name):
linesList = cleanText(name)
neutral, negative, positive = 0, 0, 0
for index, sentence in enumerate(linesList):
if re.match(r'^[\w]', sentence) is None:
continue
scores = sentiment_analyzer.polarity_scores(sentence)
scores.pop('compound', None)
maxAttribute = max(scores, key=lambda k: scores[k])
if maxAttribute == "neu":
neutral += 1
elif maxAttribute == "neg":
negative += 1
else:
positive += 1
total = neutral + negative + positive
print("Negative: {0}% | Neutral: {1}% | Positive: {2}%".format(
negative * 100 / total, neutral * 100 / total, positive * 100 / total))