Deep Learning for Natural Language Processing, Sentiment Classification of Korean Steam Reviews using BiLSTM

Natural Language Processing (NLP) is a field of artificial intelligence (AI) that enables computers to understand and interpret human language. Particularly due to advancements in Deep Learning, many innovations are occurring in the field of natural language processing. This article aims to discuss how to classify the sentiment of Korean Steam reviews using the BiLSTM (Bidirectional Long Short-Term Memory) model.

1. Overview of Natural Language Processing and Sentiment Analysis

Among the various fields of natural language processing, Sentiment Analysis is a technology that automatically detects emotions or opinions from text data. For example, determining whether a user’s written review on Steam games is positive, negative, or neutral falls into this category.

The main application areas of sentiment analysis are as follows:

  • Social media monitoring
  • Product review analysis
  • Customer feedback and service improvement
  • Political election prediction

2. Deep Learning and the BiLSTM Algorithm

Deep Learning is a method of analyzing data through multiple layers of neural networks. Compared to traditional machine learning techniques, Deep Learning can achieve better performance from larger datasets. Among them, LSTM (Long Short-Term Memory) is a deep learning model suitable for sequence data processing, providing the advantage of remembering over time.

BiLSTM is a variant of LSTM that processes a given sequence of words in both directions. That is, it reads a sequence from front to back as well as from back to front, preserving information simultaneously. This is particularly effective for sequential data such as language.

3. Data Collection and Preprocessing

To collect Korean Steam review data, it is necessary to utilize the Steam game’s API or employ web crawling techniques. The collected data is typically provided in text format, and this data needs to be properly preprocessed.

3.1 Data Crawling

Data can be crawled from the Steam website using Python’s BeautifulSoup and Requests libraries. This process allows for the efficient collection of a much larger amount of information than manually collecting data.

3.2 Data Preprocessing

Preprocessing has a significant impact on the performance of sentiment analysis models. The main preprocessing tasks usually performed are as follows:

  • Stop Word Removal: Removing meaningless words such as ‘is’, ‘are’, ‘not’, ‘of’
  • Morpheme Analysis: Using Korean morpheme analyzers such as Komoran and MeCab to separate words
  • Tokenization: Separating sentences into words or morphemes
  • Cleaning: Removing special characters, numbers, etc.
  • Embedding: Vectorizing words using methods such as Word2Vec or GloVe

4. Building the BiLSTM Model

Now, we will build the BiLSTM model based on the collected data. Deep learning libraries such as TensorFlow or PyTorch can be used. Here, we will explain based on TensorFlow.

4.1 Library Installation

!pip install tensorflow numpy pandas sklearn matplotlib

4.2 Preparing the Dataset

import pandas as pd

# Load the dataset from a CSV file
data = pd.read_csv('steam_reviews.csv')
x = data['review']  # Review text
y = data['label']    # Sentiment label (positive/negative)

4.3 Splitting the Dataset

from sklearn.model_selection import train_test_split

# Split the dataset into training and testing data
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)

4.4 Model Configuration

import tensorflow as tf

# Define the BiLSTM model
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=max_length),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences=True)),
    tf.keras.layers.GlobalMaxPooling1D(),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

4.5 Training the Model

history = model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_test, y_test))

5. Model Evaluation

To evaluate the model’s performance, predictions are made using the test data. Then, metrics such as confusion matrix and accuracy score can be used to measure the model’s performance.

from sklearn.metrics import classification_report, confusion_matrix

# Model prediction
y_pred = (model.predict(x_test) > 0.5).astype("int32")

# Performance evaluation
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))

6. Results and Discussion

After the model training is complete, we evaluate the model’s performance by visualizing the trends of accuracy and changes in loss through the learning logs. The most important aspect is the model’s performance not only on the fixed dataset but also on real data.

To improve the model, various methods can be considered. For example, hyperparameter tuning, data augmentation, and more complex network structures. Additionally, trying various embedding techniques can also yield good results.

7. Conclusion

Leveraging deep learning for natural language processing and sentiment analysis is a powerful and useful technology. In this article, we explained how to classify the sentiment of Korean Steam reviews using the BiLSTM model. Utilizing various natural language processing techniques can lead to more effective sentiment analysis.

The future sentiment analysis models will evolve through more data and better algorithms, opening new opportunities in various fields such as social media, customer service, and marketing analysis.

8. References

  • Goodfellow, Ian, et al. “Deep Learning.” MIT Press, 2016.
  • Jurafsky, Daniel, and James H. Martin. “Speech and Language Processing.” Pearson, 2019.
  • Vaswani, Ashish, et al. “Attention is all you need.” Advances in Neural Information Processing Systems, 2017.
  • Choe, Doohwan, et al. “A Survey of Sentiment Analysis in Natural Language Processing.” IEEE Access, 2020.