Sentiment Analysis of Tokopedia App Reviews

Fajar Satria

Data Scientist

pandas

Python

TensorFlow

Electronics

Sentiment Analysis of Tokopedia App Reviews📱

This project performs web scraping of Tokopedia app reviews from the Google Play Store and builds sentiment analysis models using various machine learning and deep learning approaches.

📁 Project Structure

scraping.ipynb

Notebook for scraping review data from Google Play Store using google-play-scraper.

sentiment_model.ipynb

Notebook containing:

Data exploration

Automatic sentiment labeling based on ratings

Text preprocessing

Implementation and evaluation of 3 model schemes:

TF-IDF + SVM

Word2Vec + Random Forest

LSTM (Deep Learning)

🛠️ Technologies & Libraries

Python

pandas, numpy, re, string

nltk (stopwords)

scikit-learn

gensim (Word2Vec)

tensorflow / Keras (LSTM)

google_play_scraper

🔍 Steps

1. Scraping Tokopedia Reviews

Using google_play_scraper to fetch up to 10,000 user reviews for the Tokopedia app from Google Play Store (Indonesia localization).

from google_play_scraper import reviews
Output is saved in the file data_tokopedia_reviews.csv.

2. Preprocessing & Labeling

Remove unnecessary characters, numbers, punctuation, and stopwords.

Sentiment labels are assigned based on ratings:

⭐ 1–2 → Negative

⭐ 3 → Neutral

⭐ 4–5 → Positive

3. Built Model Schemes

✅ Scheme 1: TF-IDF + SVM TF-IDF vectorization

Linear Support Vector Classifier (SVM)

Evaluation: accuracy & classification report (sklearn)

🌳 Scheme 2: Word2Vec + Random Forest Word embedding using Word2Vec (gensim)

Each review represented as the average of word vectors

Classification model: Random Forest

🧠 Scheme 3: LSTM (Deep Learning) Tokenization and sequence padding

Architecture: LSTM + Bidirectional + Dropout

Final activation: Softmax for 3 classes

📈 Results

TF-IDF + SVM Accuracy    : 0.989
Word2Vec + RF Accuracy   : 0.989
LSTM Accuracy            : 0.9905
Results may vary depending on dataset & random state.

🧪 Inference

# TF-IDF + SVM
"This product is very disappointing and does not match the description" → Prediction: NEGATIVE

# Word2Vec + RF
"fast and friendly service" → Prediction: POSITIVE

# LSTM
"This product is really cool" → Prediction: POSITIVE