Natural language processing is a technology that enables computers to understand human language, and recently, with the advancement of deep learning techniques, its possibilities have expanded even further. In particular, sentiment analysis on e-commerce platforms that have vast amounts of review data plays an important role in effectively processing customer feedback and establishing marketing strategies. This blog introduces a sentiment classification method using Naver Shopping review data.
1. What is Natural Language Processing (NLP)?
Natural Language Processing (NLP) is a field of computer science and artificial intelligence that focuses on understanding and interpreting natural language (human language). NLP consists of the following major processes:
- Text Preprocessing: This is the stage of gathering and refining data. It includes processes like tokenization, stopword removal, and stemming.
- Feature Extraction: This process involves extracting meaningful information from text and quantifying it. Techniques such as TF-IDF, Word2Vec, and BERT can be used.
- Model Training: This is the stage where data is trained using machine learning or deep learning models.
- Model Evaluation: The model’s performance is evaluated, and parameter tuning or model adjustments are made if necessary.
- Utilization of Results: Predictions for new data are made using the trained model, which are then applied to actual business scenarios.
2. Advances in Deep Learning Techniques
Deep learning is a machine learning technique based on artificial neural networks that excels at automatically learning features from data through layered structures. In recent years, network architectures such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have been effectively applied to natural language processing. In particular, models like BERT (Bidirectional Encoder Representations from Transformers) have dramatically improved the performance of natural language processing.
3. Collecting Naver Shopping Review Data
The review data from Naver Shopping contains the opinions and sentiments of various consumers. Web scraping techniques can be used to collect this data. Let’s look at how to collect the desired review data using Python’s BeautifulSoup library or the Scrapy framework.
3.1 Example of Data Collection Using BeautifulSoup
import requests
from bs4 import BeautifulSoup
url = 'https://shopping.naver.com/your_product_page'
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
reviews = soup.find_all('div', class_='review')
for review in reviews:
print(review.text)4. Data Preprocessing
The collected review data must be preprocessed to be suitable for model training. During the preprocessing stage, the following tasks are carried out:
- Tokenization: The process of separating sentences into words.
- Stopword Removal: Removing meaningless words to enhance data quality.
- Stemming: Extracting the root form of words to perform morphological analysis.
4.1 Preprocessing Example
import re
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
def preprocess(text):
# Remove special characters
text = re.sub('[^A-Za-z0-9가-힣\s]', '', text)
# Tokenization
tokens = word_tokenize(text)
# Remove stopwords
tokens = [word for word in tokens if word not in stopwords.words('korean')]
return tokens5. Building a Sentiment Classification Model
Based on the preprocessed data, we build a sentiment classification model. Let’s look at an example using a simple LSTM (Long Short-Term Memory) model to classify the sentiment of reviews as positive or negative.
5.1 Example of Building an LSTM Model
from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense
model = Sequential()
model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=max_length))
model.add(LSTM(units=128, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(units=1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])6. Model Evaluation and Performance Improvement
To evaluate the model’s performance, we separate the training data and validation data and proceed with evaluation after training. Various methods can also be applied to improve the model’s accuracy:
- Data Augmentation: Increase the amount of data through various transformations.
- Hyperparameter Tuning: Adjust the model’s hyperparameters such as learning rate and batch size.
- Transfer Learning: Use pre-trained models to enhance performance.
6.1 Evaluation Example
loss, accuracy = model.evaluate(X_test, y_test)
print(f'Test accuracy: {accuracy * 100:.2f}%')7. Interpreting and Utilizing Results
Based on the model’s results, we can analyze the Naver Shopping review data and understand consumer sentiments and trends. For example, if there is a significant amount of positive feedback for a specific product, we can use it to strengthen the marketing strategy for that product.
8. Conclusion
The natural language processing technology using deep learning is a powerful tool for effectively analyzing large volumes of data like Naver Shopping reviews. Throughout this tutorial, we have explored how to implement sentiment analysis using deep learning. We hope this provides an opportunity to effectively analyze consumer feedback and utilize it in business decision-making.
9. References
- Kim, Sang-hyung, “Deep Learning with Natural Language Processing”, Hanbit Media, 2020.
- Lee, Seong-ho, “Natural Language Processing Using Deep Learning”, Insight, 2019.
- Lee, Hae-in et al., “Machine Learning and Deep Learning Based on Python”, Information Culture Corporation, 2021.