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import streamlit as st
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
from PIL import Image
def run():
#Membuat title
st.title('Text-Based Twitter Sentiment Analysis')
#Tambahkan gambar
image = Image.open('twittersentiment.jpg')
st.image(image, caption = 'Twitter Sentiment')
#Membuat garis
st.markdown('----')
#Masukkan pandas dataframe
#Show dataframe
df = pd.read_csv('tweets-update.csv')
st.dataframe(df)
st.write('Source : https://www.kaggle.com/datasets/yasserh/twitter-tweets-sentiment-dataset')
st.markdown('----')
st.title('Exploratory Data Analysis')
#Bar Plot
st.write('#### Distribution of Sentiments')
fig_sentiments = plt.figure(figsize=(10, 6))
sns.countplot(x='sentiment', data=df)
plt.xlabel('Sentiment Label')
plt.ylabel('Count')
plt.title('Distribution of Sentiments')
st.pyplot(fig_sentiments)
# Positive Sentiment Tweets Bar
st.write('#### Distribution of Text Length for Positive Sentiment Tweets')
fig_length_positive = plt.figure(figsize=(14, 7))
# Handle NaN values in 'text_processed'
df['length'] = df['text_processed'].apply(lambda x: len(str(x).split()) if pd.notna(x) else 0)
ax1 = fig_length_positive.add_subplot(122)
sns.histplot(df[df['sentiment']=='positive']['length'], ax=ax1, color='green')
describe_positive = df.length[df.sentiment=='positive'].describe().to_frame().round(2)
ax2 = fig_length_positive.add_subplot(121)
ax2.axis('off')
font_size = 14
bbox = [0, 0, 1, 1]
table_positive = ax2.table(cellText=describe_positive.values, rowLabels=describe_positive.index, bbox=bbox, colLabels=describe_positive.columns)
table_positive.set_fontsize(font_size)
fig_length_positive.suptitle('Distribution of text length for positive sentiment tweets.', fontsize=16)
st.pyplot(fig_length_positive)
# negative Sentiment Tweets Bar
st.write('#### Distribution of Text Length for negative Sentiment Tweets')
fig_length_negative = plt.figure(figsize=(14, 7))
# Handle NaN values in 'text_processed'
df['length'] = df['text_processed'].apply(lambda x: len(str(x).split()) if pd.notna(x) else 0)
ax1 = fig_length_negative.add_subplot(122)
sns.histplot(df[df['sentiment']=='negative']['length'], ax=ax1, color='red')
describe_negative = df.length[df.sentiment=='negative'].describe().to_frame().round(2)
ax2 = fig_length_negative.add_subplot(121)
ax2.axis('off')
font_size = 14
bbox = [0, 0, 1, 1]
table_negative = ax2.table(cellText=describe_negative.values, rowLabels=describe_negative.index, bbox=bbox, colLabels=describe_negative.columns)
table_negative.set_fontsize(font_size)
fig_length_negative.suptitle('Distribution of text length for negative sentiment tweets.', fontsize=16)
st.pyplot(fig_length_negative)
# neutral Sentiment Tweets Bar
st.write('#### Distribution of Text Length for neutral Sentiment Tweets')
fig_length_neutral = plt.figure(figsize=(14, 7))
# Handle NaN values in 'text_processed'
df['length'] = df['text_processed'].apply(lambda x: len(str(x).split()) if pd.notna(x) else 0)
ax1 = fig_length_neutral.add_subplot(122)
sns.histplot(df[df['sentiment']=='neutral']['length'], ax=ax1, color='blue')
describe_neutral = df.length[df.sentiment=='neutral'].describe().to_frame().round(2)
ax2 = fig_length_neutral.add_subplot(121)
ax2.axis('off')
font_size = 14
bbox = [0, 0, 1, 1]
table_neutral = ax2.table(cellText=describe_neutral.values, rowLabels=describe_neutral.index, bbox=bbox, colLabels=describe_neutral.columns)
table_neutral.set_fontsize(font_size)
fig_length_neutral.suptitle('Distribution of text length for neutral sentiment tweets.', fontsize=16)
st.pyplot(fig_length_neutral)
# Membuat pie chart
st.write('#### Pie Chart - Sentiment Distribution')
labels = ['Neutral', 'Positive', 'Negative']
size = df['sentiment'].value_counts()
colors = ['lightgreen', 'lightskyblue', 'lightcoral']
explode = [0.01, 0.01, 0.1]
fig, axes = plt.subplots(figsize=(6, 5))
plt.pie(size, colors=colors, explode=explode,
labels=labels, shadow=True, startangle=90, autopct='%.2f%%')
plt.title('Sentiment Distribution', fontsize=20)
plt.legend()
st.pyplot(fig)
# #Membuat histogram
# st.write('#### Histogram of Age')
# fig = plt.figure(figsize=(15,5))
# sns.histplot(df['Overall'], bins = 30, kde = True)
# st.pyplot(fig)
# #membuat histogram berdasarkan inputan user
# st.write('#### Histogram berdasarkan input user')
# #kalo mau pake radio button, ganti selectbox jadi radio
# option = st.selectbox('Pilih Column : ', ('Age', 'Weight', 'Height', 'ShootingTotal'))
# fig = plt.figure(figsize= (15,5))
# sns.histplot(df[option], bins = 30, kde = True)
# st.pyplot(fig)
# #Membuat Plotly plot
# st.write('#### Plotly Plot - ValueEUR vs Overall')
# fig = px.scatter(df, x = 'ValueEUR', y = 'Overall', hover_data = ['Name', 'Age'])
# st.plotly_chart(fig)
if __name__ == '__main__':
run()
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