I have code for a Twitter streamer that performs sentiment analysis on tweets that go into a CSV file. The sentiment analysis results are then plotted on a pie chart, and what I'd like to do is also plot these on a histogram too. I've looked at a couple of posts here (https://stackoverflow.com/questions/18736978/plotting-histogram-with-matplotlib)(https://stackoverflow.com/questions/47538718/histogram-plotting-matplotlib) and a YouTube vid on how to do it and can't understand why it's not working. I'll post the whole code so you can see everything it's doing but the code for the charts is very near the bottom.
The error I receive is, "AttributeError: 'SentimentAnalysis' object has no attribute 'plotHist'" but it's laid out exactly the same as the pie chart and this plots fine. Can someone point out what I'm doing wrong please?
import csv import re import sys import tweepy import matplotlib.pyplot as plt from textblob import TextBlob import sqlite3 as lite # import models.py - was a file with CREATE statement in for SQLite global db global cursor class SentimentAnalysis: def __init__(self): self.tweets = [] # initialising empty list objects self.tweetText = [] # self.api = tweepy.API(self.auth) # Change access details below to point to own application def download_data(self): # authenticating consumerKey = '************' consumerSecret = '************' accessToken = '************' accessTokenSecret = '************' auth = tweepy.OAuthHandler(consumerKey, consumerSecret) auth.set_access_token(accessToken, accessTokenSecret) # api = tweepy.API(auth) api = tweepy.API(auth, wait_on_rate_limit=True) # input for term to be searched and how many tweets to search searchTerm = input("Enter Keyword/Tag to search about: ") NoOfTerms = int(input("Enter how many tweets to search: ")) # 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('result.csv', 'a') # Use csv writer csvWriter = csv.writer(csvFile) # creating variables to store info polarity = 0 positive = 0 wpositive = 0 spositive = 0 negative = 0 wnegative = 0 snegative = 0 neutral = 0 # iterating through tweets fetched for tweet in self.tweets: # Append to temp so that we can store in csv later. I use encode UTF-8 self.tweetText.append(self.clean_tweet(tweet.text).encode('utf-8')) # print (tweet.text.translate(non_bmp_map)) #print tweet's text analysis = TextBlob(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 0 < analysis.sentiment.polarity <= 0.3: wpositive += 1 elif 0.3 < analysis.sentiment.polarity <= 0.6: positive += 1 elif 0.6 < analysis.sentiment.polarity <= 1: spositive += 1 elif -0.3 < analysis.sentiment.polarity <= 0: wnegative += 1 elif -0.6 < analysis.sentiment.polarity <= -0.3: negative += 1 elif -1 < analysis.sentiment.polarity <= -0.6: snegative += 1 # Write to csv and close csv file csvWriter.writerow(self.tweetText) csvFile.close() # delete this for the insert SQL code bit # Attempt at SQLite code, couldn't get it right # con = lite.connect(r"C:\\Users\\Student User\\PycharmProjects\\DataScienceLabs\\tweets.db") # cur = con.cursor() # cur.execute('''CREATE TABLE tweets(created_at text, id_str text, text text)''') # commented out as models.py creates database # cur.execute("INSERT INTO tweets(?, ?, ?)", (created_at, # id_str, # text)) # con.commit() # finding average of how people are reacting 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("How people are reacting on " + searchTerm + " by analyzing " + str(NoOfTerms) + " tweets.") print() print("General Report: ") if polarity == 0: print("Neutral") elif 0 < polarity <= 0.3: print("Weakly Positive") elif 0.3 < polarity <= 0.6: print("Positive") elif 0.6 < polarity <= 1: print("Strongly Positive") elif -0.3 < polarity <= 0: print("Weakly Negative") elif -0.6 < polarity <= -0.3: print("Negative") elif -1 < 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) + "% people thought it was neutral") self.plotPieChart(positive, wpositive, spositive, negative, wnegative, snegative, neutral, searchTerm, NoOfTerms) self.plotHist(positive, wpositive, spositive, negative, wnegative, snegative, neutral) def clean_tweet(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 * float(part) / float(whole) return format(temp, '.2f') def plotPieChart(self, positive, wpositive, spositive, negative, wnegative, snegative, neutral, searchTerm, noOfSearchTerms): labels = ['Positive [' + str(positive) + '%]', 'Weakly Positive [' + str(wpositive) + '%]','Strongly Positive [' + str(spositive) + '%]', 'Neutral [' + str(neutral) + '%]', 'Negative [' + str(negative) + '%]', 'Weakly Negative [' + str(wnegative) + '%]', 'Strongly Negative [' + str(snegative) + '%]'] sizes = [positive, wpositive, spositive, neutral, negative, wnegative, snegative] colors = ['yellowgreen', 'lightgreen', 'darkgreen', 'gold', 'red', 'lightsalmon', 'darkred'] patches, texts = plt.pie(sizes, colors=colors, startangle=90) plt.legend(patches, labels, loc="best") plt.title('How people are reacting on ' + searchTerm + ' by analyzing ' + str(noOfSearchTerms) + ' Tweets.') plt.axis('equal') plt.tight_layout() plt.show() def plotHsit(self, positive, wpositive, spositive, negative, wnegative, snegative, neutral): sentiments = [positive, wpositive, spositive, negative, wnegative, snegative, neutral] plt.hist(sentiments) plt.show() if __name__== "__main__": # db_init() sa = SentimentAnalysis() sa.download_data() 
