| import streamlit as st |
|
|
|
|
| def run(): |
| st.header("🧪 Methodology") |
| st.write("How we turned ~5,400 Tokopedia reviews into actionable themes.") |
|
|
| st.markdown("#### The Pipeline") |
| st.markdown("`Reviews -> Clean text -> Embeddings -> Cluster (BERTopic) -> Themes`") |
| st.markdown( |
| """ |
| 1. **Data** - PRDECT-ID: ~5,400 Indonesian product reviews, each labeled Positive/Negative plus an emotion. |
| 2. **Preprocessing** - lowercase, strip URLs/symbols, normalize slang (`gak` -> `tidak`); Indonesian |
| stopwords are removed only when extracting the topic keywords. |
| 3. **Embeddings** - each review becomes a 384-dim vector using a multilingual language model |
| (paraphrase-multilingual-MiniLM-L12-v2). |
| 4. **Clustering** - reviews are split by sentiment, then BERTopic (UMAP + HDBSCAN) groups similar |
| vectors into topics, separately for positive and negative reviews. |
| 5. **Themes** - the many micro-topics are grouped into **8 praise** and **9 complaint** business themes. |
| """ |
| ) |
|
|
| st.markdown("#### Why BERTopic?") |
| c1, c2, c3 = st.columns(3) |
| c1.markdown("**K-Means**\n\nSimple, but needs the number of clusters up front and gave coarse, " |
| "mixed groups.") |
| c2.markdown("**LDA**\n\nClassic bag-of-words baseline; broad topics that ignore word context.") |
| c3.markdown("**BERTopic (chosen)**\n\nEmbeddings + density clustering. Best coherence and " |
| "diversity, and the finest themes.") |
|
|
| st.markdown("#### The Key Decision") |
| st.info( |
| "BERTopic's default clustering (HDBSCAN 'eom') collapsed **89% of complaints into one " |
| "cluster**. Switching to 'leaf' separated them into 9 clean themes - our single most " |
| "important tuning choice." |
| ) |
|
|
| st.markdown("#### How This App Predicts") |
| st.markdown( |
| "Your review goes through the **same cleaning**, becomes an embedding, and is matched by " |
| "**cosine similarity** to the learned topic vectors. The nearest theme - and whether it " |
| "reads as praise or a complaint - is returned, with no retraining needed." |
| ) |
|
|
