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Sentiment_analyser.py
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Sentiment_analyser.py
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# Core Packages
import sys,re
import tkinter as tk
import tweepy
#for graphing the data and display in Tkinter
import matplotlib
matplotlib.use("TkAgg")
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
#gui libraries
from tkinter import *
from tkinter import ttk
# NLP Packages
from nltk.sentiment.vader import SentimentIntensityAnalyzer
from textblob import TextBlob
import spacy
nlp = spacy.load('en_core_web_sm')
# Structure and Layout
window = Tk()
window.title("Sentiment Analyser")
window.geometry("700x600")
window.config(background='black')
# TAB LAYOUT
tab_control = ttk.Notebook(window)
tab1 = ttk.Frame(tab_control)
tab2 = ttk.Frame(tab_control)
# ADD TABS TO NOTEBOOK
tab_control.add(tab1, text='Analyser')
tab_control.add(tab2, text='About')
label1 = Label(tab1, text= 'Using NLP',padx=5, pady=5)
label1.grid(column=0, row=0)
label2 = Label(tab2, text= 'About',padx=5, pady=5)
label2.grid(column=0, row=0)
tab_control.pack(expand=1, fill='both')
about_label = Label(tab2,text="Sentiment Analysis GUI V.0.0.1 \n Akshit Desai\n@codestromer",pady=5,padx=5)
about_label.grid(column=0,row=1)
#My Twitter API Authentication Variables
consumer_key = ''
consumer_secret = ''
access_token = ''
access_token_secret = ''
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
# Clear display widget
def clear_display_result():
tab1_display.delete('1.0',END)
# Functions FOR NLP FOR TAB ONE
def get_sentiment():
raw_text = str(raw_entry.get())
tweets = tweepy.Cursor(api.search, q=raw_text, lang = "en").items(100)
polarity = 0
positive = 0
wpositive = 0
spositive = 0
negative = 0
wnegative = 0
snegative = 0
neutral = 0
NoOfTerms = 100
tweetTexts = []
# iterating through tweets fetched
for tweet in tweets:
#Append to temp so that we can store in csv later. I use encode UTF-8
tweetTexts.append(cleanTweet(tweet.text).encode('utf-8'))
analysis = TextBlob(tweet.text)
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
# finding average of how people are reacting
positive = percentage(positive, NoOfTerms)
wpositive = percentage(wpositive, NoOfTerms)
spositive = percentage(spositive, NoOfTerms)
negative = percentage(negative, NoOfTerms)
wnegative = percentage(wnegative, NoOfTerms)
snegative = percentage(snegative, NoOfTerms)
neutral = percentage(neutral, NoOfTerms)
# = pd.DataFrame(data=[tweet.text for tweet in tweets], columns=['Tweets'])
for tweetText in tweetTexts:
tab1_display.insert(tk.END,str(tweetText)+'\n')
plotPieChart(positive, wpositive, spositive, negative, wnegative, snegative, neutral, raw_text, NoOfTerms)
def cleanTweet(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())
#for finding percentage
def percentage(part, whole):
temp = 100 * float(part) / float(whole)
return format(temp, '.2f')
#plotting a graph on entered data
def plotPieChart(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']
f=Figure(figsize=(5,5),dpi=100)
f.suptitle('How people are reacting on ' + searchTerm + ' by analyzing ' + str(noOfSearchTerms) + ' Tweets.', fontsize=10)
plt=f.add_subplot(111)
patches, texts = plt.pie(sizes, colors=colors, startangle=90)
plt.legend(patches, labels, loc="best")
plt.axis('equal')
canvas = FigureCanvasTkAgg(f)
canvas.get_tk_widget().pack(side=tk.BOTTOM, fill=tk.BOTH, expand=FALSE)
# MAIN NLP TAB
l1=Label(tab1,text="Enter Text for Analysis")
l1.grid(row=1,column=0)
#input widget
raw_entry=StringVar()
entry1=Entry(tab1,textvariable=raw_entry,width=50)
entry1.grid(row=1,column=1)
# bUTTONS
button1=Button(tab1,text="Start", width=12,command=get_sentiment,bg='#03A9F4',fg='#fff')
button1.grid(row=4,column=1,padx=0,pady=10)
button1=Button(tab1,text="Clear", width=12,command=clear_display_result,bg='#03A9F4',fg='#fff')
button1.grid(row=5,column=1,padx=0,pady=10)
# Display Screen For Result
tab1_display = Text(tab1,height=8)
tab1_display.grid(row=8,column=0, columnspan=3,padx=5,pady=5)
# Allows you to edit
tab1_display.config(state=NORMAL)
window.mainloop()
# Akshit Desai @codestromer