Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,6 +1,8 @@
 import streamlit as st
 import pandas as pd
 from pathlib import Path
+from collections import Counter
+import re
 
 st.set_page_config(page_title="Customer Experience Analyzer", layout="wide")
 
@@ -48,6 +50,34 @@ if total_reviews > 0:
 else:
     st.warning("No reviews match the selected filters.")
 
+# Top complaint words
+st.subheader("Top Complaint Words")
+
+negative_reviews = filtered_df[filtered_df["sentiment"] == "negative"]["review_text"]
+
+if len(negative_reviews) > 0:
+    all_text = " ".join(negative_reviews.astype(str)).lower()
+    words = re.findall(r"\b[a-z]+\b", all_text)
+
+    stopwords = {
+        "the", "and", "was", "were", "is", "it", "to", "of", "for", "in",
+        "a", "an", "this", "that", "with", "on", "at", "my", "our", "we",
+        "i", "had", "but", "very", "so", "not", "be", "been", "are", "as",
+        "they", "them", "you", "he", "she", "his", "her", "their", "there",
+        "here", "have", "has", "from", "too", "all"
+    }
+
+    filtered_words = [word for word in words if word not in stopwords and len(word) > 2]
+
+    if filtered_words:
+        word_counts = Counter(filtered_words).most_common(10)
+        word_df = pd.DataFrame(word_counts, columns=["Word", "Count"])
+        st.bar_chart(word_df.set_index("Word"))
+    else:
+        st.info("No meaningful complaint words found.")
+else:
+    st.info("No negative reviews available for this selection.")
+
 # Example reviews
 st.subheader("Example Customer Reviews")
 
@@ -85,7 +115,7 @@ if total_reviews > 0:
         f"""
         - Out of **{total_reviews}** filtered reviews, **{positive_rate:.1f}%** are positive and **{negative_rate:.1f}%** are negative.
         - This helps management quickly assess overall customer satisfaction.
-        - Searching by keyword can help identify specific issues such as service, food, or staff.
+        - Searching by keyword can help identify specific issues in customer feedback.
         """
     )
 else:
@@ -98,9 +128,9 @@ if total_reviews > 0:
     if negative_rate > 60:
         st.warning("Customer dissatisfaction is high. Management should urgently review repeated complaints and investigate operational issues.")
     elif negative_rate > 40:
-        st.info("Customer sentiment is mixed. Management should identify the most common negative themes and improve consistency.")
+        st.info("Customer sentiment is mixed. Management should identify recurring negative themes and improve consistency.")
     else:
-        st.success("Customer sentiment is mostly positive. Management should preserve strengths and monitor new complaints.")
+        st.success("Customer sentiment is mostly positive. Management should preserve strengths and continue monitoring feedback.")
 else:
     st.write("No recommendation available.")
 
@@ -126,7 +156,13 @@ if question:
         st.write("Positive reviews suggest that customers were satisfied with their restaurant experience.")
     elif "negative" in q:
         st.write("Negative reviews suggest that customers experienced problems that may affect satisfaction and loyalty.")
+    elif "complaint" in q or "complaints" in q:
+        if len(negative_reviews) > 0 and filtered_words:
+            top_word = word_counts[0][0]
+            st.write(f"A common complaint-related word in the selected reviews is **{top_word}**.")
+        else:
+            st.write("There are no complaint words available in the current selection.")
     elif "improve" in q or "improvement" in q:
         st.write("Management should focus on recurring negative feedback and investigate the causes behind poor customer experiences.")
     else:
-        st.write("This dashboard helps management understand customer sentiment, review examples, and potential improvement areas.")
+        st.write("This dashboard helps management understand customer sentiment, complaint patterns, and possible improvement areas.")