Upload 10 files

.gitattributes

@@ -41,3 +41,5 @@ src/src/Negative[[:space:]]-[[:space:]]Topic[[:space:]]Activities[[:space:]]Over
 src/src/Positive[[:space:]]-[[:space:]]Topic[[:space:]]Activities[[:space:]]Over[[:space:]]Time.png filter=lfs diff=lfs merge=lfs -text
 src/Negative[[:space:]]-[[:space:]]Wordcloud.png filter=lfs diff=lfs merge=lfs -text
 src/Positive[[:space:]]-[[:space:]]Wordcloud.png filter=lfs diff=lfs merge=lfs -text
+src/Negative[[:space:]]-[[:space:]]Top[[:space:]]Activity[[:space:]]Over[[:space:]]Time[[:space:]]10.png filter=lfs diff=lfs merge=lfs -text
+src/Positive[[:space:]]-[[:space:]]Topic[[:space:]]Activities[[:space:]]Over[[:space:]]Time[[:space:]]10.png filter=lfs diff=lfs merge=lfs -text

src/eda.py

@@ -74,18 +74,18 @@ def run():
     st.write("## Topic Modeling Results")
     col1, col2 = st.columns(2)
     with col1:
-        st.image(os.path.join(BASE_DIR, "Negative - Top Words Distributions.png"), caption="Negative - Top Words Distributions")
+        st.image(os.path.join(BASE_DIR, "Negative - Top Words Distributions 10.png"), caption="Negative - Top Words Distributions")
     with col2:
-        st.image(os.path.join(BASE_DIR, "Positive - Top Words Distributions.png"), caption="Positive - Top Words Distributions")
+        st.image(os.path.join(BASE_DIR, "Positive - Top Words Distributions 10.png"), caption="Positive - Top Words Distributions")
 
     col1, col2 = st.columns(2)
     with col1:
-        st.image(os.path.join(BASE_DIR, "Negative - Topic Activities Over Time.png"), caption="Negative - Topic Activities Over Time")
+        st.image(os.path.join(BASE_DIR, "Negative - Topic Activities Over Time 10.png"), caption="Negative - Topic Activities Over Time")
     with col2:
-        st.image(os.path.join(BASE_DIR, "Positive - Topic Activities Over Time.png"), caption="Positive - Topic Activities Over Time")
+        st.image(os.path.join(BASE_DIR, "Positive - Topic Activities Over Time 10.png"), caption="Positive - Topic Activities Over Time")
 
     col1, col2 = st.columns(2)
     with col1:
-        st.image(os.path.join(BASE_DIR, "Negative - Topics Weights.png"), caption="Negative - Topics Weights")
+        st.image(os.path.join(BASE_DIR, "Negative - Topics Weights 10.png"), caption="Negative - Topics Weights")
     with col2:
-        st.image(os.path.join(BASE_DIR, "Positive - Topics Weights.png"), caption="Positive - Topics Weights")
+        st.image(os.path.join(BASE_DIR, "Positive - Topics Weights 10.png"), caption="Positive - Topics Weights")

src/fastopic_negative_model_10.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d4da14366fd0678b0befbc3e2cf3340dc9568981e56f402c8f95ca5ca4f01511
+size 114569213

src/fastopic_positive_model_10.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a9f18982fee97966b9229a9dbd37545409061dba4122d87e4a4cab2c051a4fca
+size 124939691

src/prediction_compile.py

@@ -94,19 +94,29 @@ def text_preprocessing(text):
 
 # --- Topic Labels ---
 topic_labels_neg = {
-    1: "meal and entertainment service",
-    2: "refund, cancellation, and booking tickets policy",
-    3: "business class/premium facility",
-    4: "baggage limits and price",
-    5: "hidden charges"
+    0: "Service Attitude",
+    1: "Ticket Price",
+    2: "In-Flight Accommodation",
+    3: "Boarding & Luggage Issues",
+    4: "Refund & Payment Difficulties",
+    5: "Meal Quality",
+    6: "Accessibility & Assistance",
+    7: "Safety & Hygiene",
+    8: "Seat Comfort",
+    9: "Quality of Amenities"
 }
 
 topic_labels_pos = {
-    1: "good food and crew service",
-    2: "excellent economy seat",
-    3: "refund and cancellation policy",
-    4: "meals quality",
-    5: "accommodation and assistance"
+    0: "Destination-based compliment",
+    1: "Seat & cabin comfort",
+    2: "Destination-based compliment",
+    3: "Transit accommodation",
+    4: "Meals & in-flight services",
+    5: "Meals & in-flight services",
+    6: "Seat & cabin comfort / Aircraft condition",
+    7: "Destination-based compliment",
+    8: "Miscellaneous experiences",
+    9: "Destination-based compliment"
 }
 
 # --- Streamlit App ---
@@ -190,12 +200,12 @@ def run():
         st.write("### Topic Modeling")
         if sentiment_label == "Negative":
             probs = topic_model_neg.transform([text])[0]
-            topic_id = int(np.argmax(probs)) + 1
+            topic_id = int(np.argmax(probs))
             topic_name = topic_labels_neg.get(topic_id, "Unknown Topic")
             st.write("**Using Negative Model**")
         else:
             probs = topic_model_pos.transform([text])[0]
-            topic_id = int(np.argmax(probs)) + 1
+            topic_id = int(np.argmax(probs))
             topic_name = topic_labels_pos.get(topic_id, "Unknown Topic")
             st.write("**Using Positive Model**")