techykajal

labels =  ['Negative_Trump', 'Negative_Biden'] 
sizes = lis_neg
explode = (0.1, 0.1)

fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode=explode, labels = labels, autopct = '%1.1f%%', shadow = True, startangle=90)
ax1.set_title('Negative tweets on both the handles')
plt.show()

techykajal

# Start with one review:
text = str(df_subset_biden.text)
# Create and generate a word cloud image:
wordcloud = WordCloud(max_font_size=100, max_words=500,scale=10,relative_scaling=.6,background_color="black", colormap = "rainbow").generate(text)

# Display the generated image:
plt.figure(figsize=(15,10))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()

techykajal

# Start with one review:
text = str(df_subset_trump.text)
# Create and generate a word cloud image:
wordcloud = WordCloud(max_font_size=100, max_words=500, scale=10, relative_scaling=.6, background_color="black", colormap = "rainbow").generate(text)

# Display the generated image:
plt.figure(figsize=(15,10))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()

techykajal

most_negative2 = df_subset_biden[df_subset_biden.Sentiment_Polarity == -1].text.head()
neg_txt2 = list(most_negative2)
neg2 = df_subset_biden[df_subset_biden.Sentiment_Polarity == -1].Sentiment_Polarity.head()
neg_pol2 = list(neg2)

fig = go.Figure(data=[go.Table(columnorder = [1,2],
                               columnwidth = [50,400],
                               header=dict(values=['Polarity','Most Negative Replies on Biden\'s handle'],
                               fill_color='paleturquoise',
                               align='left'),

techykajal

most_positive2 = df_subset_biden[df_subset_biden.Sentiment_Polarity == 1].text.tail()
pos_txt2 = list(most_positive2)
pos2 = df_subset_biden[df_subset_biden.Sentiment_Polarity == 1].Sentiment_Polarity.tail()
pos_pol2 = list(pos2)

fig = go.Figure(data=[go.Table(columnorder = [1,2],
                               columnwidth = [50,400],
                               header=dict(values=['Polarity','Most Positive Replies on Biden\'s handle'],
                               fill_color='paleturquoise',
                               align='left'),

techykajal

most_negative1 = df_subset_trump[df_subset_trump.Sentiment_Polarity == -1].text.head()
neg_txt1 = list(most_negative1)
neg1 = df_subset_trump[df_subset_trump.Sentiment_Polarity == -1].Sentiment_Polarity.head()
neg_pol1 = list(neg1)

fig = go.Figure(data=[go.Table(columnorder = [1,2],
                               columnwidth = [50,400],
                               header=dict(values=['Polarity','Most Negative Replies on Trump\'s handle'],
                               fill_color='paleturquoise',
                               align='left'),

techykajal

most_positive1 = df_subset_trump[df_subset_trump.Sentiment_Polarity == 1].text.head()
pos_txt1 = list(most_positive1)
pos1 = df_subset_trump[df_subset_trump.Sentiment_Polarity == 1].Sentiment_Polarity.head()
pos_pol1 = list(pos1)

fig = go.Figure(data=[go.Table(columnorder = [1,2], 
                               columnwidth = [50,400],
                               header=dict(values=['Polarity','Most Positive Replies on Trump\'s Handle'],
                               fill_color='paleturquoise',
                               align='left'),

techykajal

Politicians = ['Donald Trump', 'Joe Biden']
lis_pos = [positive_per1, positive_per2]
lis_neg = [negative_per1, negative_per2]

fig = go.Figure(data=[
    go.Bar(name='Positive', x=Politicians, y=lis_pos),
    go.Bar(name='Negative', x=Politicians, y=lis_neg)
])
# Change the bar mode
fig.update_layout(barmode='group')

techykajal

# Let's make both the datasets balanced now. So we will just take 1000 rows from both datasets and drop rest of them.

# Donald Trump
np.random.seed(10)
remove_n =324
drop_indices = np.random.choice(Trump_reviews.index, remove_n, replace=False)
df_subset_trump = Trump_reviews.drop(drop_indices)
df_subset_trump.shape

# Joe Biden

techykajal

new2 = Biden_reviews.groupby('Expression Label').count()
x = list(new2['Sentiment_Polarity'])
y = list(new2.index)
tuple_list = list(zip(x,y))

df = pd.DataFrame(tuple_list, columns=['x','y'])

df['color'] = 'blue'
df['color'][1] = 'red'
df['color'][2] = 'green'