import streamlit as st import pandas as pd import matplotlib.pyplot as plt from sklearn.feature_extraction.text import CountVectorizer import seaborn as sns import plotly.express as px import plotly.io as pio import plotly.graph_objects as go # Set page configuration st.set_page_config(layout="wide") # Function to load and clean data def load_and_clean_data(): df1 = pd.read_csv("data/reviewed_social_media_english.csv") df2 = pd.read_csv("data/reviewed_news_english.csv") df3 = pd.read_csv("data/tamil_social_media.csv") df4 = pd.read_csv("data/tamil_news.csv") # Concatenate dataframes and clean data df_combined = pd.concat([df1, df2, df3, df4]) # Replace 'nan' and 'None' with numpy NaN for removal df_combined['Domain'] = df_combined['Domain'].replace({"MUSLIM": "Muslim", "nan": pd.NA, "None": pd.NA, "Other-Ethnic": "Other-Ethnicity"}) # Specific replacements for 'Sentiment' and 'Discrimination' df_combined['Sentiment'] = df_combined['Sentiment'].replace({"nan": pd.NA, "None": pd.NA, "No": pd.NA}) df_combined['Discrimination'] = df_combined['Discrimination'].replace({"nan": pd.NA, "None": pd.NA, "No": pd.NA}) # Drop rows with NA values in 'Domain', 'Sentiment', and 'Discrimination' df_combined.dropna(subset=['Domain', 'Sentiment', 'Discrimination'], inplace=True) return df_combined df = load_and_clean_data() # Page navigation setup page_names = [" GESI Overview", "Sentiment Analysis", "Discrimination Analysis", "Channel Analysis"] page = st.sidebar.selectbox("Choose a page", page_names) # Sidebar Filters domain_options = df['Domain'].dropna().unique() channel_options = df['Channel'].dropna().unique() sentiment_options = df['Sentiment'].dropna().unique() discrimination_options = df['Discrimination'].dropna().unique() domain_filter = st.sidebar.multiselect('Select Domain', options=domain_options, default=domain_options) channel_filter = st.sidebar.multiselect('Select Channel', options=channel_options, default=channel_options) sentiment_filter = st.sidebar.multiselect('Select Sentiment', options=sentiment_options, default=sentiment_options) discrimination_filter = st.sidebar.multiselect('Select Discrimination', options=discrimination_options, default=discrimination_options) # Apply filters df_filtered = df[(df['Domain'].isin(domain_filter)) & (df['Channel'].isin(channel_filter)) & (df['Sentiment'].isin(sentiment_filter)) & (df['Discrimination'].isin(discrimination_filter))] # Define a color palette for consistent visualization styles color_palette = px.colors.sequential.Viridis # Visualisation for Domain Distribution def create_pie_chart(df, column, title): fig = px.pie(df, names=column, title=title, hole=0.35) fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), legend=dict(x=0.1, y=1), font=dict(size=10)) fig.update_traces(marker=dict(colors=color_palette)) return fig # Visualization for Distribution of Gender versus Ethnicity def create_gender_ethnicity_distribution_chart(df): df['GenderOrEthnicity'] = df['Domain'].apply(lambda x: "Gender: Women & LGBTQIA+" if x in ["Women", "LGBTQIA+"] else "Ethnicity") fig = px.pie(df, names='GenderOrEthnicity', title='Distribution of Gender versus Ethnicity', hole=0.35) fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), legend=dict(x=0.1, y=1), font=dict(size=10)) return fig # Visualization for Sentiment Distribution Across Domains def create_sentiment_distribution_chart(df): domain_counts = df.groupby(['Domain', 'Sentiment']).size().reset_index(name='counts') domain_counts = domain_counts.sort_values('counts') # Reverse the color scheme color_map = {'Negative': 'red', 'Positive': 'blue', 'Neutral': 'lightblue'} fig = px.bar(domain_counts, x='Domain', y='counts', color='Sentiment', color_discrete_map=color_map, title="Sentiment Distribution Across Domains", barmode='stack') fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), xaxis_title="Domain", yaxis_title="Counts", font=dict(size=10)) return fig # Visualization for Correlation between Sentiment and Discrimination def create_sentiment_discrimination_grouped_chart(df): # Creating a crosstab of 'Sentiment' and 'Discrimination' crosstab_df = pd.crosstab(df['Sentiment'], df['Discrimination']) # Check if 'Yes' and 'No' are in the columns after the crosstab operation value_vars = crosstab_df.columns.intersection(['Discriminative', 'Non Discriminative']).tolist() # If 'No' is not in columns, it will not be included in melting melted_df = pd.melt(crosstab_df.reset_index(), id_vars='Sentiment', value_vars=value_vars, var_name='Discrimination', value_name='Count') # Proceeding to plot only if we have data to plot if not melted_df.empty: fig = px.bar(melted_df, x='Sentiment', y='Count', color='Discrimination', barmode='group', title="Sentiment vs. Discrimination") fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), xaxis_title="Sentiment", yaxis_title="Count", font=dict(size=10)) return fig else: return "No data to display for the selected filters." # Function for Top Domains with Negative Sentiment Chart def create_top_negative_sentiment_domains_chart(df): domain_counts = df.groupby(['Domain', 'Sentiment']).size().unstack(fill_value=0) domain_counts.sort_values(by='Negative', ascending=False, inplace=True) domain_counts_subset = domain_counts.iloc[:3, [0]] domain_counts_subset = domain_counts_subset.rename(columns={domain_counts_subset.columns[0]: 'Count'}) domain_counts_subset = domain_counts_subset.reset_index() colors = ['limegreen', 'crimson', 'darkcyan'] fig = px.bar(domain_counts_subset, x='Count', y='Domain', title='Top Domains with Negative Sentiment', color='Domain', orientation='h', color_discrete_sequence=colors) fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), xaxis_title="Negative sentiment content Count", yaxis_title="Domain", font=dict(size=10)) return fig # Function for Key Phrases in Negative Sentiment Content Chart def create_key_phrases_negative_sentiment_chart(df): cv = CountVectorizer(ngram_range=(3,3), stop_words='english') trigrams = cv.fit_transform(df['Content'][df['Sentiment'] == 'Negative']) count_values = trigrams.toarray().sum(axis=0) ngram_freq = pd.DataFrame(sorted([(count_values[i], k) for k, i in cv.vocabulary_.items()], reverse=True)) ngram_freq.columns = ['frequency', 'ngram'] fig = px.bar(ngram_freq.head(10), x='frequency', y='ngram', orientation='h', title='Key phrases in Negative Sentiment Content') fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), xaxis_title="Frequency", yaxis_title="Trigram", font=dict(size=10)) return fig # Function for Key Phrases in Positive Sentiment Content Chart def create_key_phrases_positive_sentiment_chart(df): # Filter the DataFrame for positive sentiments and drop any rows with NaN in 'Content' positive_df = df[df['Sentiment'] == 'Positive'].dropna(subset=['Content']) # Create a CountVectorizer instance cv = CountVectorizer(ngram_range=(3, 3), stop_words='english') # Apply CountVectorizer only on non-null content trigrams = cv.fit_transform(positive_df['Content']) # Sum the frequency of each n-gram and create a DataFrame count_values = trigrams.toarray().sum(axis=0) ngram_freq = pd.DataFrame(sorted([(count_values[i], k) for k, i in cv.vocabulary_.items()], reverse=True)) ngram_freq.columns = ['frequency', 'ngram'] # Create the bar chart fig = px.bar(ngram_freq.head(10), x='frequency', y='ngram', orientation='h', title='Key phrases in Positive Sentiment Content') # Update layout settings to fit and look better fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), xaxis_title="Frequency", yaxis_title="Trigram", font=dict(size=10)) return fig # Function for Prevalence of Discriminatory Content Chart def create_prevalence_discriminatory_content_chart(df): domain_counts = df.groupby(['Domain', 'Discrimination']).size().unstack(fill_value=0) fig = px.bar(domain_counts, x=domain_counts.index, y=['Discriminative', 'Non-Discriminative'], barmode='group', title='Prevalence of Discriminatory Content') fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), xaxis_title="Domain", yaxis_title="Count", font=dict(size=10)) return fig # Function for Top Domains with Discriminatory Content Chart def create_top_discriminatory_domains_chart(df): domain_counts = df.groupby(['Domain', 'Discrimination']).size().unstack(fill_value=0) domain_counts.sort_values(by='Discriminative', ascending=False, inplace=True) domain_counts_subset = domain_counts.iloc[:3] domain_counts_subset = domain_counts_subset.rename(columns={'Discriminative': 'Count'}) fig = px.bar(domain_counts_subset, x='Count', y=domain_counts_subset.index, orientation='h', title='Top Domains with Discriminatory Content') fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), xaxis_title="Discriminatory Content Count", yaxis_title="Domain", font=dict(size=10)) return fig # Function for Channel-wise Sentiment Over Time Chart def create_sentiment_distribution_by_channel_chart(df): sentiment_by_channel = df.groupby(['Channel', 'Sentiment']).size().reset_index(name='counts') color_map = {'Positive': 'blue', 'Neutral': 'lightblue', 'Negative': 'red'} fig = px.bar(sentiment_by_channel, x='Channel', y='counts', color='Sentiment', title="Sentiment Distribution by Channel", barmode='group', color_discrete_map=color_map) fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), xaxis_title="Channel", yaxis_title="Counts", font=dict(size=10), title_x=0.5) return fig # Function for Channel-wise Distribution of Discriminative Content Chart def create_channel_discrimination_chart(df): channel_discrimination = df.groupby(['Channel', 'Discrimination']).size().unstack(fill_value=0) fig = px.bar(channel_discrimination, x=channel_discrimination.index, y=['Discriminative', 'Non-Discriminative'], barmode='group') fig.update_layout(title='Channel-wise Distribution of Discriminative Content', margin=dict(l=20, r=20, t=50, b=20), font=dict(size=10), title_x=0.5) return fig # Function for rendering dashboard def render_dashboard(page, df_filtered): if page == " GESI Overview": st.title(" GESI Overview Dashboard") col1, col2 = st.columns(2) with col1: st.plotly_chart(create_pie_chart(df_filtered, 'Domain', 'Distribution of Domains')) with col2: st.plotly_chart(create_gender_ethnicity_distribution_chart(df_filtered)) col3, col4 = st.columns(2) with col3: st.plotly_chart(create_sentiment_distribution_chart(df_filtered)) with col4: chart = create_sentiment_discrimination_grouped_chart(df_filtered) if isinstance(chart, str): st.write(chart) else: st.plotly_chart(chart) elif page == "Sentiment Analysis": st.title("Sentiment Analysis Dashboard") col1, col2 = st.columns(2) with col1: st.plotly_chart(create_sentiment_distribution_chart(df_filtered)) with col2: st.plotly_chart(create_top_negative_sentiment_domains_chart(df_filtered)) col3, col4 = st.columns(2) with col3: st.plotly_chart(create_key_phrases_negative_sentiment_chart(df_filtered)) with col4: st.plotly_chart(create_key_phrases_positive_sentiment_chart(df_filtered)) elif page == "Discrimination Analysis": st.title("Discrimination Analysis Dashboard") col1, col2 = st.columns(2) with col1: st.plotly_chart(create_prevalence_discriminatory_content_chart(df_filtered)) with col2: st.plotly_chart(create_top_discriminatory_domains_chart(df_filtered)) elif page == "Channel Analysis": st.title("Channel Analysis Dashboard") col1, col2 = st.columns(2) with col1: st.plotly_chart(create_sentiment_distribution_by_channel_chart(df_filtered)) with col2: st.plotly_chart(create_channel_discrimination_chart(df_filtered)) # Render the selected dashboard page render_dashboard(page, df_filtered)