[Python] Python Sentiment Analysis on Excel File

I'm very new to python but I would like to run a sentiment analysis on customer reviews I have collected in a form of excel files. I got this code originally from a friend who was doing sentiment analysis on Tweets. Could someone guide me on which parts to cut out to exclude the Tweets aspect and what I need to add to the code in order to make it run Sentiment Analysis on the data I've collected?

import sys,tweepy,csv,re
from textblob import TextBlob
import matplotlib.pyplot as plt


class SentimentAnalysis:

def __init__(self):
self.tweets = []
self.tweetText = []

def DownloadData(self):
# authenticating
consumerKey = 'JUTrQyuKQuIDoEBoJRtSHo1xA'
consumerSecret = 'XOiTV47BzE1LgWlUaoldEV72RTILDBluQ5Wz21pBQ5bobDCSgP'
accessToken = '1146742026420944897-haxc4zgGcQFJeZLlTBW1hlzlTfW4Z9'
accessTokenSecret = 'a6j9Vz4DSQpTc1uycEvMxXm5G59fPy2e86ep5LNLSL9Jo'
auth = tweepy.OAuthHandler(consumerKey, consumerSecret)
auth.set_access_token(accessToken, accessTokenSecret)
api = tweepy.API(auth)

# input for term to be searched and how many tweets to search
searchTerm = input("Enter Keyword/Tag to search about: #barkbox")
NoOfTerms = 500

# searching for tweets
self.tweets = tweepy.Cursor(api.search, q=searchTerm, lang = "en").items(NoOfTerms)

# Open/create a file to append data to
csvFile = open('resultA.csv', 'w')

# Use csv writer
csvWriter = csv.writer(csvFile)


# creating some variables to store info
polarity = 0
positive = 0
wpositive = 0
spositive = 0
negative = 0
wnegative = 0
snegative = 0
neutral = 0
totalNeg = 0
totalPos = 0


# iterating through tweets fetched
for extended_tweet in self.tweets:
#Append to temp so that we can store in csv later. I use encode UTF-8
self.tweetText.append(self.cleanTweet(extended_tweet.text).encode('utf-8'))
# print (tweet.text.translate(non_bmp_map)) #print tweet's text
analysis = TextBlob(extended_tweet.text)
# print(analysis.sentiment) # print tweet's polarity
polarity += analysis.sentiment.polarity # adding up polarities to find the average later

if (analysis.sentiment.polarity == 0): # adding reaction of how people are reacting to find average later
neutral += 1
elif (analysis.sentiment.polarity > 0 and analysis.sentiment.polarity <= 0.3):
wpositive += 1
elif (analysis.sentiment.polarity > 0.3 and analysis.sentiment.polarity <= 0.6):
positive += 1
elif (analysis.sentiment.polarity > 0.6 and analysis.sentiment.polarity <= 1):
spositive += 1
elif (analysis.sentiment.polarity > -0.3 and analysis.sentiment.polarity <= 0):
wnegative += 1
elif (analysis.sentiment.polarity > -0.6 and analysis.sentiment.polarity <= -0.3):
negative += 1
elif (analysis.sentiment.polarity > -1 and analysis.sentiment.polarity <= -0.6):
snegative += 1


# Write to csv and close csv file

csvWriter.writerow(self.tweetText)
csvFile.close()

# finding average of how people are reacting
totalPos = self.percentage((positive + wpositive + spositive), NoOfTerms)
totalNeg = self.percentage((negative + wnegative + snegative), NoOfTerms)

positive = self.percentage(positive, NoOfTerms)
wpositive = self.percentage(wpositive, NoOfTerms)
spositive = self.percentage(spositive, NoOfTerms)
negative = self.percentage(negative, NoOfTerms)
wnegative = self.percentage(wnegative, NoOfTerms)
snegative = self.percentage(snegative, NoOfTerms)
neutral = self.percentage(neutral, NoOfTerms)

# finding average reaction
polarity = polarity / NoOfTerms

# printing out data
print("General Reaction on " + searchTerm )
print()
print("General Report: ")

if (polarity == 0):
print("Neutral")
elif (polarity > 0 and polarity <= 0.3):
print("Weakly Positive")
elif (polarity > 0.3 and polarity <= 0.6):
print("Positive")
elif (polarity > 0.6 and polarity <= 1):
print("Strongly Positive")
elif (polarity > -0.3 and polarity <= 0):
print("Weakly Negative")
elif (polarity > -0.6 and polarity <= -0.3):
print("Negative")
elif (polarity > -1 and polarity <= -0.6):
print("Strongly Negative")

print()
print("Detailed Report: ")
print(str(positive) + "% people thought it was positive")
print(str(wpositive) + "% people thought it was weakly positive")
print(str(spositive) + "% people thought it was strongly positive")
print(str(negative) + "% people thought it was negative")
print(str(wnegative) + "% people thought it was weakly negative")
print(str(snegative) + "% people thought it was strongly negative")
print(str(neutral) + "% Overall Neutral")
print(str(positive + wpositive + spositive) + "% Overall Positive")
print(str(negative + wnegative + snegative) + "% Overall Negative")



self.plotPieChart(positive, wpositive, spositive, negative, wnegative, snegative, neutral, searchTerm, NoOfTerms,totalPos, totalNeg)


def cleanTweet(self, tweet):
# Remove Links, Special Characters etc from tweet
return ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t]) | (\w +:\ / \ / \S +)", " ", tweet).split())

# function to calculate percentage
def percentage(self, part, whole):
temp = 100 * part / whole
return format(temp, '.2f')

def plotPieChart(self, positive, wpositive, spositive, negative, wnegative, snegative, neutral, searchTerm, noOfSearchTerms, totalPos, totalNeg):
labels = ['Neutral Positive [' + str(positive) + '%]', 'Weakly Positive [' + str(wpositive) + '%]','Strongly Positive [' + str(spositive) + '%]', 'Neutral [' + str(neutral) + '%]',
'Neutral Negative [' + str(negative) + '%]', 'Weakly Negative [' + str(wnegative) + '%]', 'Strongly Negative [' + str(snegative) + '%]']
sizes = [positive, wpositive, spositive, neutral, negative, wnegative, snegative]
colors = ['blue','lightblue','darkblue', 'grey', 'red','lightsalmon','darkred']
patches, texts = plt.pie(sizes, colors=colors, startangle=90)
plt.legend(patches, labels, loc="best")
plt.title('Peoples Reaction on "' + searchTerm + '".')
plt.axis('equal')
plt.xlabel(('Total Positive [' + str(totalPos) + '%] | Total Negative [' + str(totalNeg) + '%]' ))
plt.tight_layout()
plt.show()



if __name__== "__main__":
sa = SentimentAnalysis()
sa.DownloadData()

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