Topic Modeling

Use Case:  Tag customer feedback with categories or topics. 

Overview :  Customers provide feedback through product reviews and ranking, comments, and questions. Customer feedback directly impacts online product visibility, search ranking, seller reputation, and Gross Merchandise Sales(GMS). E-Commerce platforms use crowdsourced information to improve product positioning and promotional strategies. However, bad actors can exploit this system to harm product or seller reputations and promote counterfeit , substandard or low-quality products.  This narrative will focus on automatically processing new customer feedback to filter out known issues, remove noise, eliminate duplicates, and finally prioritize the most important insights using ML techniques.

Domain:  Online marketplaces, including e-commerce platforms, online auction marketplaces, and crowdsourced aggregation networks.

Note: Gross Merchandise Sales (GMS), also known as Gross Merchandise Value (GMV) or Gross Merchandise Volume, is the total dollar value of all items sold through a marketplace or e-commerce platform over a specified period, before deducting fees, expenses, returns, or shipping. 

GMS (or GMV)= (Number of items sold) × (Selling price per item) 

Key Challenges :

1. Interpretability - extracted topics are often difficult for humans to interpret, as topics may not align with clear, coherent themes or may mix unrelated concepts

2. Algorithm Selection - there are many topic modeling algorithms (e.g., LDA, LSA, NMF) with no universal guidance for optimal choice. Outcomes will very depending on the algorithm choice.  Algorithm selection process can be expensive in terms of time and resources

3. Data Preprocessing - raw text is noisy, with irrelevant words, stop words, foreign language phrases, abusive language, undocumented domain specific abbreviations, and inconsistent formatting affecting topic quality

4. Evaluation - lack of standardized, reliable evaluation metrics makes it more difficult to compare and contrast various algorithms. In general, coherence and accuracy are hard to measure, and results may not be reproducible

5. Scalability/Adaptability - handling large, dynamic, or multi-modal (image, speech, video, etc.) datasets efficiently, and adapting to evolving topics over time

6. LLMs - the emergence of LLMs has significantly transformed topic modeling, with advancements in the field rapidly evolving

Proposed Solution includes :

1. Use preprocessing steps like stop word and generic word removal to improve topic clarity. Employ explainable topic modeling techniques and human-in-the-loop (HITL) validation

2. Experiment with multiple algorithms and hyperparameter settings. Use cross-validation, coherence scores, or domain-specific metrics (conversation rates, domain specific risk scores, etc.) for most effective algorithm selection. Allocate enough time to experiment with at least two competing algorithms

3. Apply robust preprocessing, tokenization, lemmatization, stop word and punctuation removal, and relative pruning. Address domain-specific language, slang, and abbreviation clarifications in the data pre-processing stage. Enforce consistency and standardization across the document collection 

4. Use multiple evaluation metrics (e.g., topic coherence, cluster accuracy). Compare model output with known labels or human judgment when possible. Automate evaluation metrics generation and reporting process to foster shared understanding across the data science, engineering and business stakeholders

5. Start with a small sample set and use batch-wise or online algorithms for scalability. Explore dynamic topic models for evolving data

6. . Keep track of latest advances and algorithmic enhancements

Considerations :

1. Topic Modeling requires iterative refinement and domain expertise for evaluation. To develop clarity and deeper insights, we will need custom visualization tools, dashboards or manual labeling workflows (HITL)

2. Model selection and tuning can be computationally intensive. Topic Modeling requires trial-and-error and careful output quality evaluation

3. Preprocessing pipelines must be tailored to data sources and domain rules, including security and legal compliance. Its quality directly affects model performance and interpretability. In topic modeling domain, false evaluations are costly with direct downstream impact (rejected loan applications, wrong diagnosis, etc.) 

4.  Topic model evaluation is subjective and often requires domain expertise. Automated metrics may miss nuances; expect iteration and occasional course corrections and plan accordingly

5. . Enterprise grade topic modeling initiatives may need to update ML models regularly to track new and / or shifting topics

Application of Topic Modeling : 

1.  Content summarization, document classification, and knowledge discovery in large text document collections

2. Automated topic discovery in research, legal, or news archives

3. Social media analysis, customer feedback mining, and sentiment analysis

4.  Academic research, where reproducibility and rigor in topic assignments are critical

5. Real-time trend analysis, news aggregation, and monitoring social media streams

Key Concepts:

1. Corpus: A collection of documents.

2. Topics: Abstract themes or categories within the corpus.

3. Document-Term Matrix: A matrix representing the frequency of words in each document.

4. Latent Dirichlet Allocation (LDA): A popular topic modeling algorithm.

5. Non-negative Matrix Factorization (NMF): Another common topic modeling algorithm

Prototype : 

Use case covered - Tag customer feedback with categories or topics. We will tag Useful customer feedback as 1, and  Noise as 0. The use case will include - 1/ Noise filtering using a classifier, 2 / Duplicate detection using semantic similarity and 3/ basic prioritization strategy.

Out of scope -  Advanced prioritization strategies and re-training the ML model based on the Human in the Loop [HITL] feedback, exposing model predictions via API based interface and the dashboard reporting

Prototype Source code is based on the sentence-transformers library (built on top of Hugging Face Transformers) 

import pandas as pd

import numpy as np

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import classification_report

from sentence_transformers import SentenceTransformer, util

import torch

# Step 2: Load Sample Data (replace with real dataset)

data = pd.DataFrame({

    'feedback': [

        'App crashes frequently',

        'Love the new design!',

        'Loading time is too long',

        'Duplicate message shown twice',

        'Need dark mode option',

        'This feedback is irrelevant spam message'

    ],

    'label': [1, 1, 1, 1, 1, 0]  # 1 = Useful, 0 = Noise

})

# Step 3: Text Embedding for Noise Filter

model = SentenceTransformer('all-MiniLM-L6-v2')

data['embedding'] = data['feedback'].apply(lambda x: model.encode(x, convert_to_tensor=True))

# Prepare dataset

X = torch.stack(data['embedding'].to_list())

y = data['label'].values

# Step 4: Train/Test Split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Step 5: Logistic Regression Classifier (using numpy arrays)

X_train_np = X_train.numpy()

X_test_np = X_test.numpy()

clf = LogisticRegression(max_iter=1000)

clf.fit(X_train_np, y_train)

data

feedback label embedding length priority_score

==========================================================================================================

0 App crashes frequently 1 [tensor(0.0823), tensor(-0.0213), tensor(0.043... 22 32

1 Love the new design! 1 [tensor(-0.0554), tensor(0.0877), tensor(0.038... 20 30

2 Loading time is too long 1 [tensor(0.0123), tensor(0.0293), tensor(0.0122... 24 34

3 Duplicate message shown twice 1 [tensor(0.0054), tensor(-0.0162), tensor(0.072... 29 39

4 Need dark mode option 1 [tensor(0.0081), tensor(-0.0130), tensor(0.045... 21 31

5 This feedback is irrelevant spam message 0 [tensor(-0.0154), tensor(0.0081), tensor(0.003... 40 40

==========================================================================================================

# Step 7: Duplicate Detection (semantic similarity)

pairs = []

similar_threshold = 0.85

for i in range(len(data)):

    for j in range(i + 1, len(data)):

        score = util.cos_sim(data.loc[i, 'embedding'], data.loc[j, 'embedding']).item()

        if score > similar_threshold:

            pairs.append((i, j, score))

print("\nPotential Duplicates (semantic similarity > 0.85):")

for i, j, score in pairs:

    print(f"[{i}] {data.loc[i, 'feedback']}\n[{j}] {data.loc[j, 'feedback']}\n→ Similarity: {score:.2f}\n")

# Step 8: Placeholder for Prioritization (to be enhanced)

data['length'] = data['feedback'].apply(len)

data['priority_score'] = data['length'] + data['label'] * 10

prioritized = data.sort_values(by='priority_score', ascending=False)

print("\nTop Prioritized Feedback:")

print(prioritized[['feedback', 'priority_score']])

Top Prioritized Feedback:

                                   feedback priority_score

===============================================================================

5  This feedback is irrelevant spam message              40

3            Duplicate message shown twice              39

2                 Loading time is too long             34

0                   App crashes frequently              32

4                     Need dark mode option             31

1                     Love the new design!             30

===============================================================================

#testing the model

v1 = "This feedback is irrelevant spam message" # input

enc1 = model.encode(v1, convert_to_tensor=True) # encoding

y_pred1 = clf.predict(enc1.numpy().reshape(1, -1)) # prediction

y_pred[0] # output

np.int64(1)

Summary : Topic modeling helps businesses identify key themes in large volumes of unstructured text—like reviews, support tickets, and social media—without needing labeled data. This unsupervised technique groups words that appear in similar contexts, revealing hidden topics and turning raw text into actionable insights for better decisions and efficiency