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.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

README.md

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+# *Web Dashboard For Sentiment Analysis* 
+![WebDashboard0](images/i0.png)
+![WebDashboard1](images/i1.png)
+![WebDashboard2](images/i2.png)
+![WebDashboard3](images/i3.png)
+
+
+### Set up guide
+> First Get the repository files Downloading or Clonning the repository
+> Now open the folder using any ide or editor and select python. Now navigate to console and type
+```bash
+streamlit run app/app.py
+```
+> It will open in localhost server. Now you can use it easily.
+
+### Customize Heading and Title of your Task
+```python
+t.title("Sentiment Analysis Tweets About US Airlines")
+st.sidebar.title("Sentiment Analysis Tweets About US Airlines")
+st.markdown(" This Application is a streamlit Dashboard to analyze Sentiment of Airlines Tweets in US 🐦")
+st.sidebar.markdown(" This Application is a streamlit Dashboard to analyze Sentiment of Airlines Tweets in US 🐦")
+
+```
+Change the title,sidebar title,markdown and sidebar markdown according to your project.
+
+### Environment 
+Make sure you have all necessery python library installed. For this projects you need 
+* Numpy
+* Pandas
+* Streamlit
+* Plotly
+* Matplotlib
+* Scikit-learn
+
+if you don't have these libraries go to python or anaconda prompt and paste these:
+```bash
+pip install numpy
+pip install pandas
+pip install streamlit
+pip install plotly
+pip install matplotlib
+pip install sklearn
+```
+
+
+
+### Get touch with Me
+Connect- [Linkedin](https://linkedin.com/in/rakibhhridoy) <br>
+Website- [RakibHHridoy](https://rakibhhridoy.github.io)
+
+

app/app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+from wordcloud import WordCloud, STOPWORDS
+import matplotlib.pyplot as plt
+
+
+st.title("Sentiment Analysis Tweets About US Airlines")
+st.sidebar.title("Sentiment Analysis Tweets About US Airlines")
+st.markdown(" This Application is a streamlit Dashboard to analyze Sentiment of Airlines Tweets in US 🐦")
+st.sidebar.markdown(" This Application is a streamlit Dashboard to analyze Sentiment of Airlines Tweets in US 🐦")
+
+DATA_URL = ("./data/Tweets.csv")
+
+@st.cache(persist = True)   # so it will cached the data for no reloding data again and again
+
+def load_data():
+    data_matrix = pd.read_csv(DATA_URL)
+    data_matrix['tweet_created'] = pd.to_datetime(data_matrix['tweet_created'])
+
+    return data_matrix
+
+df = load_data()
+
+st.sidebar.subheader("Show random tweet")
+random_tweet = st.sidebar.radio("Sentiment", ("positive", 'neutral', 'negative'))
+st.sidebar.markdown(df.query('airline_sentiment == @random_tweet')[["text"]].sample(n=1).iat[0, 0])
+
+
+st.sidebar.markdown("### Number of Tweet By Sentiment")
+select = st.sidebar.selectbox('Visualization Type', ["Histogram", 'Pie Chart'], key = '1')
+sentiment_count = df['airline_sentiment'].value_counts()
+sentiment_count = pd.DataFrame({'Sentiment': sentiment_count.index, "Tweets": sentiment_count.values})
+
+if not st.sidebar.checkbox("Hide", True):
+    st.markdown('### Number of tweets by sentiment')
+    if select =='Histogram':
+        fig = px.bar(sentiment_count, x = 'Sentiment', y = 'Tweets',color = 'Tweets', height= 500)
+        st.plotly_chart(fig)
+
+    else:
+        fig = px.pie(sentiment_count, values= 'Tweets', names= 'Sentiment')
+        st.plotly_chart(fig)
+
+
+st.sidebar.subheader("When and Where are users tweeting from")
+hour = st.sidebar.slider("Hour of day",0, 23)
+modified_data = df[df['tweet_created'].dt.hour == hour]
+
+if not st.sidebar.checkbox("Close", True, key ='1'):
+    st.markdown('### Tweets loactions based on the time')
+    st.markdown("%i tweets between %i:00 and %i:00" % (len(modified_data), hour, (hour+1)%24))
+    st.map(modified_data)
+    if st.sidebar.checkbox("Show raw data", False):
+        st.write(modified_data)
+
+
+st.sidebar.subheader('Breakdown airline tweets by sentimnet')
+choice = st.sidebar.multiselect('Pick airlines', ('US Airways', 'Unitedd', 'American', 'Southwest', 'Delta', 'Virgin America'), key= '0')
+
+
+if len(choice) >0:
+    choice_data  = df[df.airline.isin(choice)]
+    fig_choice = px.histogram(choice_data, x = 'airline', y = 'airline_sentiment', histfunc = 'count', color = 'airline_sentiment', facet_col = 'airline_sentiment', labels = {'airline_sentiment': 'tweets'}, height = 600, width= 800)
+    st.plotly_chart(fig_choice)
+
+
+
+st.sidebar.header("World Cloud")
+word_sentiment = st.sidebar.radio('Display word cloud for what sentiment?', ('positive', 'neutral', 'negative'))
+
+if not st.sidebar.checkbox("CLose", True, key = '3'):
+    st.header("Word Cloud for %s sentiment" % (word_sentiment))
+    df = df[df['airline_sentiment']== word_sentiment]
+    words = ' '.join(df['text'])
+    processed_words = ' '.join([word for word in words.split() if 'http' not in word and not word.startswith('@') and word != 'RT'])
+    wordcloud = WordCloud(stopwords = STOPWORDS, background_color = 'white',height = 640, width = 800).generate(processed_words)
+    plt.imshow(wordcloud)
+    plt.xticks([])
+    plt.yticks([])
+    st.pyplot()