# -*- coding: utf-8 -*- """ # MANIFESTO ANALYSIS """ ##IMPORTING LIBRARIES import random import matplotlib.pyplot as plt import nltk from nltk.tokenize import word_tokenize,sent_tokenize from nltk.corpus import stopwords from nltk.stem.porter import PorterStemmer from nltk.stem import WordNetLemmatizer from nltk.corpus import stopwords from nltk.tokenize import word_tokenize from nltk.probability import FreqDist from cleantext import clean import textract import urllib.request import nltk.corpus from nltk.text import Text import io from io import StringIO,BytesIO import sys import pandas as pd import cv2 import re from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator from textblob import TextBlob from PIL import Image import os import gradio as gr from zipfile import ZipFile import contractions import unidecode nltk.download('stopwords') nltk.download('punkt') nltk.download('wordnet') nltk.download('words') """## PARSING FILES""" #def Parsing(parsed_text): #parsed_text=parsed_text.name #raw_party =parser.from_file(parsed_text) # raw_party = raw_party['content'] # return clean(raw_party) def Parsing(parsed_text): parsed_text=parsed_text.name raw_party =textract.process(parsed_text, encoding='ascii',method='pdfminer') return clean(raw_party) #Added more stopwords to avoid irrelevant terms stop_words = set(stopwords.words('english')) stop_words.update('ask','much','thank','etc.', 'e', 'We', 'In', 'ed','pa', 'This','also', 'A', 'fu','To','5','ing', 'er', '2') """## PREPROCESSING""" def clean_text(text): ''' The function which returns clean text ''' text = text.encode("ascii", errors="ignore").decode("ascii") # remove non-asciicharacters text=unidecode.unidecode(text)# diacritics remove text=contractions.fix(text) # contraction fix text = re.sub(r"\n", " ", text) text = re.sub(r"\n\n", " ", text) text = re.sub(r"\t", " ", text) text = re.sub(r"/ ", " ", text) text = text.strip(" ") text = re.sub(" +", " ", text).strip() # get rid of multiple spaces and replace with a single text = [word for word in text.split() if word not in stop_words] text = ' '.join(text) return text # text_Party=clean_text(raw_party) def Preprocess(textParty): ''' Removing special characters extra spaces ''' text1Party = re.sub('[^A-Za-z0-9]+', ' ', textParty) #Removing all stop words pattern = re.compile(r'\b(' + r'|'.join(stopwords.words('english')) + r')\b\s*') text2Party = pattern.sub('', text1Party) # fdist_cong = FreqDist(word_tokens_cong) return text2Party ''' Using Concordance, you can see each time a word is used, along with its immediate context. It can give you a peek into how a word is being used at the sentence level and what words are used with it. ''' def concordance(text_Party,strng): word_tokens_party = word_tokenize(text_Party) moby = Text(word_tokens_party) resultList = [] for i in range(0,1): save_stdout = sys.stdout result = StringIO() sys.stdout = result moby.concordance(strng,lines=4,width=82) sys.stdout = save_stdout s=result.getvalue().splitlines() return result.getvalue() def normalize(d, target=1.0): raw = sum(d.values()) factor = target/raw return {key:value*factor for key,value in d.items()} def fDistance(text2Party): ''' Most frequent words search ''' word_tokens_party = word_tokenize(text2Party) #Tokenizing fdistance = FreqDist(word_tokens_party).most_common(10) mem={} for x in fdistance: mem[x[0]]=x[1] return normalize(mem) def fDistancePlot(text2Party,plotN=15): ''' Most Frequent Words Visualization ''' word_tokens_party = word_tokenize(text2Party) #Tokenizing fdistance = FreqDist(word_tokens_party) plt.title('Frequency Distribution') plt.axis('off') plt.figure(figsize=(4,3)) fdistance.plot(plotN) plt.tight_layout() buf = BytesIO() plt.savefig(buf) buf.seek(0) img1 = Image.open(buf) plt.clf() return img1 def DispersionPlot(textParty): ''' Dispersion PLot ''' word_tokens_party = word_tokenize(textParty) #Tokenizing moby = Text(word_tokens_party) fdistance = FreqDist(word_tokens_party) word_Lst=[] for x in range(5): word_Lst.append(fdistance.most_common(5)[x][0]) plt.axis('off') plt.title('Dispersion Plot') plt.figure(figsize=(4,3)) moby.dispersion_plot(word_Lst,color="#EF6D6D") plt.tight_layout() buf = BytesIO() plt.savefig(buf) buf.seek(0) img = Image.open(buf) plt.clf() return img def getSubjectivity(text): ''' Create a function to get the polarity ''' return TextBlob(text).sentiment.subjectivity def getPolarity(text): ''' Create a function to get the polarity ''' return TextBlob(text).sentiment.polarity def getAnalysis(score): if score < 0: return 'Negative' elif score == 0: return 'Neutral' else: return 'Positive' url = "http://library.bjp.org/jspui/bitstream/123456789/2988/1/BJP-Election-english-2019.pdf" path_input = "./Bjp_Manifesto_2019.pdf" urllib.request.urlretrieve(url, filename=path_input) url="https://drive.google.com/uc?id=1BLCiy_BWilfVdrUH8kbO-44DJevwO5CG&export=download" path_input = "./Aap_Manifesto_2019.pdf" urllib.request.urlretrieve(url, filename=path_input) url="https://drive.google.com/uc?id=1HVZvTtYntl0YKLnE0cwu0CvAIRhXOv60&export=download" path_input = "./Congress_Manifesto_2019.pdf" urllib.request.urlretrieve(url, filename=path_input) def analysis(Manifesto,Search): raw_party = Parsing(Manifesto) text_Party=clean_text(raw_party) text_Party= Preprocess(text_Party) df = pd.DataFrame(raw_party.split('\n'), columns=['Content']) df['Subjectivity'] = df['Content'].apply(getSubjectivity) df['Polarity'] = df['Content'].apply(getPolarity) df['Analysis on Polarity'] = df['Polarity'].apply(getAnalysis) df['Analysis on Subjectivity'] = df['Subjectivity'].apply(getAnalysis) plt.title('Sentiment Analysis') plt.xlabel('Sentiment') plt.ylabel('Counts') plt.figure(figsize=(4,3)) df['Analysis on Polarity'].value_counts().plot(kind ='bar',color="#FF9F45") plt.tight_layout() buf = BytesIO() plt.savefig(buf) buf.seek(0) img1 = Image.open(buf) plt.clf() plt.figure(figsize=(4,3)) df['Analysis on Subjectivity'].value_counts().plot(kind ='bar',color="#B667F1") plt.tight_layout() buf = BytesIO() plt.savefig(buf) buf.seek(0) img2 = Image.open(buf) plt.clf() wordcloud = WordCloud(max_words=2000, background_color="white",mode="RGB").generate(text_Party) plt.figure(figsize=(4,3)) plt.imshow(wordcloud, interpolation="bilinear") plt.axis("off") plt.tight_layout() buf = BytesIO() plt.savefig(buf) buf.seek(0) img3 = Image.open(buf) plt.clf() fdist_Party=fDistance(text_Party) img4=fDistancePlot(text_Party) img5=DispersionPlot(text_Party) searchRes=concordance(text_Party,Search) searChRes=clean(searchRes) searChRes=searchRes.replace(Search,"\u0332".join(Search)) return searChRes,fdist_Party,img1,img2,img3,img4,img5 Search_txt=gr.inputs.Textbox() filePdf = gr.inputs.File() text = gr.outputs.Textbox(label='SEARCHED OUTPUT') mfw=gr.outputs.Label(label="Most Relevant Topics") plot1=gr.outputs. Image(label='Sentiment Analysis') plot2=gr.outputs.Image(label='Subjectivity Analysis') plot3=gr.outputs.Image(label='Word Cloud') plot4=gr.outputs.Image(label='Frequency Distribution') plot5=gr.outputs.Image(label='Dispersion Plot') io=gr.Interface(fn=analysis, inputs=[filePdf,Search_txt], outputs=[text,mfw,plot1,plot2,plot3,plot4,plot5], title='Manifesto Analysis',examples=[['./Bjp_Manifesto_2019.pdf','development'],['./Aap_Manifesto_2019.pdf','delhi'],['./Congress_Manifesto_2019.pdf','safety']]) io.launch(debug=False,share=True) #,examples=[['./Bjp_Manifesto_2019.pdf','india'],['./Aap_Manifesto_2019.pdf',],['./Congress_Manifesto_2019.pdf',]]