devamitranjan

{
  "name": "my-package",
  "private": true,
  "version": "0.0.0",
  "type": "module",
  "scripts": {
    "dev": "vite",
    "start": "vite",
    "start:mock": "VITE_MOCK_SERVER=true vite",
    "build": "tsc && vite build",

devamitranjan

const getRandomNumberOfTodos = () => {
  const todos = [];
  for (let i = 0; i < Math.floor(Math.random() * 10); i++) {
    todos.push({
      id: i,
      title: `My Todo - ${i}`,
      description: `This is the description of the todo -${i}`
    });
  }

devamitranjan

import os
import googleapiclient.discovery

# Fetching the data from Youtube API
def google_api(key,vidId):
    os.environ["OAUTHLIB_INSECURE_TRANSPORT"] = "1"

    api_service_name = "youtube"
    api_version = "v3"
    DEVELOPER_KEY = key

devamitranjan

import matplotlib.pyplot as plt
from sklearn.feature_extraction import text 
from wordcloud import WordCloud

def generate_word_clouds(df):
  allWords = ' '.join([twts for twts in df['Comments']])
  wordCloud = WordCloud(stopwords = text.ENGLISH_STOP_WORDS ,width=1000, height=600, random_state=21, max_font_size=110).generate(allWords)
  plt.imshow(wordCloud, interpolation="bilinear")
  plt.axis('off')
  plt.show()

devamitranjan

def print_neutral_comments():
  print('Printing neutral comments:\n')
  sortedDF = df.sort_values(by=['Polarity']) 
  for i in range(0, sortedDF.shape[0] ):
    if( sortedDF['Analysis'][i] == 'Neutral'):
      print(str(i+1) + '> '+ sortedDF['Comments'][i])
      print()
print_neutral_comments()

devamitranjan

def print_negative_comments():
  print('Printing negative comments:\n')
  sortedDF = df.sort_values(by=['Polarity']) 
  for i in range(0, sortedDF.shape[0] ):
    if( sortedDF['Analysis'][i] == 'Negative'):
      print(str(i+1) + '> '+ sortedDF['Comments'][i])
      print()
print_negative_comments()

devamitranjan

def print_positive_comments():
  print('Printing positive comments:\n')
  sortedDF = df.sort_values(by=['Polarity']) 
  for i in range(0, sortedDF.shape[0] ):
    if( sortedDF['Analysis'][i] == 'Positive'):
      print(str(i+1) + '> '+ sortedDF['Comments'][i])
      print()
print_positive_comments()

devamitranjan

analysis = lambda polarity: 'Positive' if polarity > 0 else 'Neutral' if polarity == 0 else 'Negative' 

def analysis_based_on_polarity(df):
  df['Analysis'] = df['Polarity'].apply(analysis)
  return df
  
df = analysis_based_on_polarity(df)
df

devamitranjan

from textblob.blob import TextBlob

def find_polarity_of_single_comment(text):
   return  TextBlob(text).sentiment.polarity

def find_polarity_of_every_comment(df):  
  df['Polarity'] = df['Comments'].apply(find_polarity_of_single_comment)
  return df 

df = find_polarity_of_every_comment(df)

devamitranjan

from textblob.blob import TextBlob

def find_subjectivity_on_single_comment(text):
  return TextBlob(text).sentiment.subjectivity
   
def apply_subjectivity_on_all_comments(df):
  df['Subjectivity'] = df['Comments'].apply(find_subjectivity_on_single_comment)
  return df 

df = apply_subjectivity_on_all_comments(df)