Update app.py

app.py

@@ -23,6 +23,7 @@ if "trending_top_n" not in st.session_state:
 if "change_top_n" not in st.session_state:
     st.session_state.change_top_n = DEFAULT_TOP_N
 
+
 #############################
 # PAGE 1: Historical Sentiment
 #############################
@@ -106,7 +107,14 @@ def plot_dual_axes(
     return fig
 
 def run_historical_sentiment(ticker):
-    st.write("Shows how Stocktwits and Twitter sentiment changed over time.")
+    # Explanation of this page
+    st.write("**Historical Sentiment**")
+    st.write(
+        "Use this page to see how sentiment changes over time for a specific ticker. "
+        "It retrieves past data on sentiment, posts, comments, likes, and impressions "
+        "from two social platforms. This can help spot long-term shifts or trends."
+    )
+    
     df = get_historical_sentiment(ticker)
     if df.empty:
         st.error("No data found for " + ticker)
@@ -115,7 +123,7 @@ def run_historical_sentiment(ticker):
     common_range = [df["date"].min(), df["date"].max()]
 
     st.subheader(f"{ticker} Sentiment Scores")
-    st.write("Compares the sentiment scores from Stocktwits and Twitter.")
+    st.write("Compares sentiment scores from two social channels over the selected date range.")
     fig1 = plot_dual_axes(
         df, "date", "stocktwitsSentiment", "twitterSentiment",
         f"{ticker} Sentiment Scores",
@@ -125,7 +133,7 @@ def run_historical_sentiment(ticker):
     st.plotly_chart(fig1, use_container_width=True)
 
     st.subheader(f"{ticker} Posts")
-    st.write("Compares how many posts appear on each platform.")
+    st.write("Tracks the number of posts on each platform. High volumes can hint at heightened interest.")
     fig2 = plot_dual_axes(
         df, "date", "stocktwitsPosts", "twitterPosts",
         f"{ticker} Posts",
@@ -135,7 +143,7 @@ def run_historical_sentiment(ticker):
     st.plotly_chart(fig2, use_container_width=True)
 
     st.subheader(f"{ticker} Comments")
-    st.write("Compares the number of comments on Stocktwits and Twitter.")
+    st.write("Shows how many comments occurred. This may reveal engagement depth.")
     fig3 = plot_dual_axes(
         df, "date", "stocktwitsComments", "twitterComments",
         f"{ticker} Comments",
@@ -145,7 +153,7 @@ def run_historical_sentiment(ticker):
     st.plotly_chart(fig3, use_container_width=True)
 
     st.subheader(f"{ticker} Likes")
-    st.write("Compares how many likes each platform received.")
+    st.write("Compares the likes on each platform. More likes can signal positive reception.")
     fig4 = plot_dual_axes(
         df, "date", "stocktwitsLikes", "twitterLikes",
         f"{ticker} Likes",
@@ -155,7 +163,7 @@ def run_historical_sentiment(ticker):
     st.plotly_chart(fig4, use_container_width=True)
 
     st.subheader(f"{ticker} Impressions")
-    st.write("Compares total impressions from Stocktwits and Twitter.")
+    st.write("Shows overall impressions. Higher impressions may reflect broader visibility.")
     fig5 = plot_dual_axes(
         df, "date", "stocktwitsImpressions", "twitterImpressions",
         f"{ticker} Impressions",
@@ -164,6 +172,7 @@ def run_historical_sentiment(ticker):
     )
     st.plotly_chart(fig5, use_container_width=True)
 
+
 #############################
 # PAGE 2: Trending Sentiment
 #############################
@@ -249,15 +258,24 @@ def plot_trending(df, title, top_n):
     st.plotly_chart(fig, use_container_width=True)
 
 def run_trending_sentiment(top_n):
-    st.write("Shows trending sentiment. Two charts: bullish and bearish.")
+    # Explanation of this page
+    st.write("**Trending Sentiment**")
+    st.write(
+        "Use this page to see which symbols rank highest in bullish or bearish sentiment right now. "
+        "This helps pinpoint active tickers with strong positive or negative views."
+    )
+
     st.write("## Bullish Trending")
+    st.write("Displays the top bullish tickers and their current sentiment.")
     bullish_df = fetch_trending_sentiment(sentiment_type="bullish", source="stocktwits")
     plot_trending(bullish_df, title="Bullish Trending", top_n=top_n)
 
     st.write("## Bearish Trending")
+    st.write("Displays the top bearish tickers and their current sentiment.")
     bearish_df = fetch_trending_sentiment(sentiment_type="bearish", source="stocktwits")
     plot_trending(bearish_df, title="Bearish Trending", top_n=top_n)
 
+
 #############################
 # PAGE 3: Sentiment Change
 #############################
@@ -343,15 +361,24 @@ def plot_change_sentiment(df, title, top_n):
     st.plotly_chart(fig, use_container_width=True)
 
 def run_change_sentiment(top_n):
-    st.write("Shows how sentiment scores have changed. Two charts: bullish and bearish.")
+    # Explanation of this page
+    st.write("**Sentiment Change**")
+    st.write(
+        "Use this page to see if sentiment is trending up or down for each ticker. "
+        "Compare the current sentiment to how much it has shifted. This can reveal rapid changes."
+    )
+
     st.write("## Bullish Sentiment Change")
+    st.write("Shows bullish sentiment and recent momentum shifts in that sentiment.")
     bullish_df = fetch_change_sentiment(sentiment_type="bullish", source="stocktwits")
     plot_change_sentiment(bullish_df, title="Bullish Sentiment Change", top_n=top_n)
 
     st.write("## Bearish Sentiment Change")
+    st.write("Shows bearish sentiment and recent momentum shifts in that sentiment.")
     bearish_df = fetch_change_sentiment(sentiment_type="bearish", source="stocktwits")
     plot_change_sentiment(bearish_df, title="Bearish Sentiment Change", top_n=top_n)
 
+
 #############################
 # MAIN APP
 #############################
@@ -359,23 +386,25 @@ def run_change_sentiment(top_n):
 def main():
     st.set_page_config(page_title="Social Sentiment Analysis", layout="wide")
     st.title("Trending Social Sentiment")
+
+    # Overall introduction to the app
     st.write(
-        "This tool covers historical, trending, and changing sentiment "
-        "from Stocktwits and Twitter."
+        "Use this dashboard to explore historical, trending, and changing sentiment data. "
+        "You can research how mention counts and sentiments evolve for different symbols."
     )
 
     with st.sidebar.expander("Navigation and Options", expanded=True):
         page = st.radio(
             "Select Analysis Page",
             ("Historical Sentiment", "Trending Sentiment", "Sentiment Change"),
-            help="Choose the type of sentiment analysis."
+            help="Choose which sentiment analysis you want."
         )
 
         if page == "Historical Sentiment":
             ticker = st.text_input(
                 "Ticker Symbol",
                 value=st.session_state.historical_ticker,
-                help="Enter a ticker symbol."
+                help="Enter a ticker symbol for historical data."
             )
             if st.button("Run Historical Sentiment Analysis"):
                 st.session_state.historical_run = True
@@ -410,21 +439,21 @@ def main():
         if st.session_state.historical_run:
             run_historical_sentiment(st.session_state.historical_ticker)
         else:
-            st.info("Please enter a ticker and click the Run button.")
+            st.info("Enter a ticker and click the button.")
 
     elif page == "Trending Sentiment":
         st.header("Trending Social Sentiment")
         if st.session_state.trending_run:
             run_trending_sentiment(st.session_state.trending_top_n)
         else:
-            st.info("Please pick a top N and click the Run button.")
+            st.info("Pick a top N and click the button.")
 
     elif page == "Sentiment Change":
         st.header("Social Sentiment Change")
         if st.session_state.change_run:
             run_change_sentiment(st.session_state.change_top_n)
         else:
-            st.info("Please pick a top N and click the Run button.")
+            st.info("Pick a top N and click the button.")
 
 if __name__ == "__main__":
     main()