Japanese Text Mining with Python: Guide & Sample Code

1. Introduction

2. Fundamentals of Text Mining

What is Text Mining?

• Business Analysis: Analyzing customer reviews and conducting competitive market research.
• Social Media Analysis: Understanding trends and opinions from tweets and comments.
• Academic Research: Extracting themes and keywords from literature data.

Key Methods

1. Morphological Analysis

• A method that splits text into individual words. It is especially important for Japanese analysis, using morphological analysis tools (e.g., MeCab, Janome).
• Example Use: Extracting frequent words from product reviews to analyze customer needs.

1. Sentiment Analysis

• Analyzes whether the text expresses positive, negative, or neutral sentiment. It is often applied to data from social networks and review sites.
• Example Use: Classifying the sentiment of tweets to measure customer satisfaction.

1. Topic Modeling

• A technique that extracts latent topics from text data. Algorithms such as LDA (Latent Dirichlet Allocation) are used.
• Example Use: Classifying news articles by topic to visualize overall trends.

1. Word Cloud

• A method for visualizing the words contained in text data. Frequently occurring words appear larger, allowing visual grasp of data characteristics.
• Example Use: Extracting main themes from event survey data and using them in presentation materials.

Examples of Text Mining Applications

• Retail Industry: Extracting features that customers prioritize from product reviews.
• Healthcare: Gathering opinions on treatments from patient comments.
• Marketing: Analyzing opinions about campaigns from social media data.

3. Setting Up the Environment in Python

Required Libraries

1. pandas

• A fundamental library used for data manipulation and management. It’s handy when handling text data in CSV format and similar.
• Installation method: pip install pandas

1. MeCab

• A library used for Japanese morphological analysis. MeCab splits text into word units and provides detailed information.
• Installation method (Windows):
  1. Download the installer from the official MeCab website and install it.
  2. Install the Python library: pip install mecab-python3

1. wordcloud

• A library for generating word clouds. It’s useful for visualizing frequently occurring words.
• Installation method: pip install wordcloud

1. ​matplotlib

• A library used for plotting and visualization. It helps when visualizing data.
• Installation method: pip install matplotlib

1. scikit-learn

• Provides various machine‑learning algorithms such as topic modeling and sentiment analysis.
• Installation method: pip install scikit-learn

Using Google Colab

1. What is Google Colab?

• A free Python execution environment provided by Google that runs in the browser.
• Its features include:
  • No installation required.
  • Free access to GPUs and TPUs.
  • Easy code sharing.

1. ​Steps to start Google Colab

• While logged into your Google account, go to the official Google Colab page.
• Click “New Notebook” to launch a Python environment.

1. Installing libraries In Google Colab, you can easily install libraries. Example:

   !pip install pandas mecab-python3 wordcloud matplotlib scikit-learn

4. MeCab setup (for Japanese parsing) When using MeCab, you need to install a dictionary. Run the following code to set it up.

   !apt-get install -y mecab mecab-ipadic-utf8 libmecab-dev

Things to Keep in Mind When Building the Environment

• Handling Japanese Data: Be aware of character‑encoding issues unique to Japanese. Use data saved in UTF‑8 format.
• Performance: When dealing with large datasets, Google Colab or a server environment is more suitable than a local setup.

4. Practical: Text Mining with Python

Data Collection and Preprocessing

Data Collection

• CSV file: Prepare review or survey data in CSV format.
• Web scraping: Use Python’s requests and BeautifulSoup to retrieve data from websites.
• API: Use APIs from Twitter or news sites to collect text data.

import pandas as pd

# Load CSV file
data = pd.read_csv('sample_text_data.csv')
print(data.head())

Data Preprocessing

• Removal of symbols and numbers
• Removal of whitespace and unnecessary line breaks
• Removal of Japanese-specific stopwords (e.g., “no”, “ga”, “wa”)

import re

def preprocess_text(text):
    # Remove symbols and numbers
    text = re.sub(r'[0-9０-９]', '', text)
    text = re.sub(r'[!"#$%&'()*+,-./:;<=>?@[]^_`{|}~]', '', text)
    # Remove whitespace
    text = text.strip()
    return text

# Apply preprocessing
data['cleaned_text'] = data['text'].apply(preprocess_text)
print(data['cleaned_text'].head())

Morphological Analysis

import MeCab

# Prepare MeCab
mecab = MeCab.Tagger('-Ochasen')

# Sample text
text = "I am studying text mining with Python."

# Morphological analysis
parsed_text = mecab.parse(text)
print(parsed_text)

Extracting and Visualizing Frequent Words

Counting Frequent Words

from collections import Counter

# Create word list
words = ["Python", "text", "analysis", "Python", "data", "analysis"]

# Count frequent words
word_counts = Counter(words)
print(word_counts)

Generating a Word Cloud

from wordcloud import WordCloud
import matplotlib.pyplot as plt

# Generate word cloud
text = " ".join(words)
wordcloud = WordCloud(font_path='/path/to/japanese/font', background_color="white").generate(text)

# Display word cloud
plt.figure(figsize=(10, 6))
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis("off")
plt.show()

Sentiment Analysis

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

# Sample data
texts = ["This product is wonderful!", "It was a very bad experience", "It's an average service"]
labels = [1, 0, 2]  # 1: Positive, 0: Negative, 2: Neutral

# Vectorize texts
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)

# Classify with Naive Bayes
model = MultinomialNB()
model.fit(X, labels)

# Predict new text
new_text = ["This product is not bad"]
new_X = vectorizer.transform(new_text)
prediction = model.predict(new_X)
print(prediction)

Topic Modeling

from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import TfidfVectorizer

# Sample data
texts = ["Text mining with Python", "Text analysis and data analysis", "Fundamentals of data science"]

# Vectorize
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(texts)

# Extract topics with LDA
lda = LatentDirichletAllocation(n_components=2, random_state=42)
lda.fit(X)

# Display topics
for idx, topic in enumerate(lda.components_):
    print(f"Topic {idx}:")
    print([vectorizer.get_feature_names_out()[i] for i in topic.argsort()[:-5 - 1:-1]])

5. Application Examples

Analysis of Product Reviews

Example: Extract Frequently Used Keywords

• Analyze frequent words to clarify customers’ points of interest regarding the product.
• Compare frequent words between positive and negative reviews.

• Understand which features customers prefer.
• Analyze negative reviews to identify improvement points.

Analysis of Social Media Data

Example: Using Sentiment Analysis to Gauge Reputation

• Classify tweet content as positive or negative to measure impressions of the brand.
• Track the impact of campaigns and new products in real time.

• Measure the effectiveness of advertising campaigns.
• Identify topics that consumers focus on and leverage them in marketing strategies.

Classification and Topic Analysis of News Articles

Example: News Classification Using Topic Modeling

• Classify news articles into categories such as politics, economy, sports, etc.
• Use topic modeling to understand reporting trends.

• Make investment decisions based on trend analysis.
• Organize information in news aggregators.

Applications in the Healthcare Sector

Example: Analyzing Patient Feedback

• Use text mining to understand what treatments or care patients are seeking.
• Utilize sentiment analysis to identify issues for improving patient satisfaction.

• Analyze evaluations and improvement points of medical institutions.
• Conduct trend analysis on specific symptoms and treatments.

Utilization in the Education Sector

Example: Analyzing Student Feedback

• Analyze text data to measure comprehension and satisfaction of classes.
• Visualize frequently used words and phrases to understand student needs.

• Improve course content and design new educational programs.
• Propose personalized tutoring based on students’ learning styles.

Other Application Areas

• Financial Industry: Analyze customer inquiries to provide appropriate support.
• Legal Sector: Improve efficiency by analyzing contracts and case law data.
• Entertainment: Predict upcoming trends through analysis of movie and music reviews.

6. Frequently Asked Questions (FAQ)

Q1: What do I need to start text mining with Python?

1. Basic knowledge of Python: Knowing how to install Python and having basic coding skills will help you proceed smoothly.
2. Development environment: Using Google Colab or Jupyter Notebook makes running code easy.
3. Required libraries: Install libraries such as pandas, MeCab, and wordcloud (see the “Setting Up the Python Environment” section for details).

Q2: Which library should I use for Japanese morphological analysis?

• MeCab: A high-precision, customizable analysis tool. Choosing the appropriate dictionary can improve accuracy.
• Janome: Easy to install and usable without special configuration, making it a good choice for beginners.
• SudachiPy: Supports the latest dictionaries and is robust against orthographic variations.

• Beginners should try Janome; if you need customization, use MeCab; for advanced analysis, consider SudachiPy.

Q3: What should I watch out for when analyzing Japanese data?

1. Character encoding: Data is typically saved in UTF-8. Verify that you are using the appropriate encoding to prevent garbled text.
2. Stop words: Removing frequently occurring particles and auxiliary verbs such as “の”, “が”, and “は” can lead to more meaningful analysis.
3. Orthographic variation: The same word may appear in different forms, e.g., “東京” and “とうきょう”. Use normalization tools to handle this.

Q4: I get an error when performing morphological analysis on Google Colab. How can I fix it?

1. MeCab installation error: You may be missing required dependency libraries. Run the code below to install them.

   !apt-get install -y mecab mecab-ipadic-utf8 libmecab-dev

2. Dictionary configuration: If the dictionary isn’t installed correctly, morphological analysis won’t work. Ensure the IPA dictionary is included.
3. Specify the correct path: MeCab requires the path to the dictionary during setup. Provide the correct path and try again.

Q5: Are there ways to improve text mining results?

1. Proper stop-word settings: Removing frequently occurring meaningless words improves analysis accuracy.
2. Introduce a custom dictionary: Using a dictionary tailored to specific industries or terminology enhances analysis accuracy.
3. Data cleaning: Remove unnecessary information (symbols, numbers, etc.) from the data before analysis.

Q6: How can I efficiently process large amounts of data?

1. Chunking: Split the data into smaller chunks and process them sequentially.
2. Parallel processing: Use Python’s multiprocessing module to run multiple processes in parallel.
3. Leverage cloud environments: Process large-scale data using cloud services such as Google Colab or AWS.

Q7: Can beginners in Python still do text mining?

7. Summary and Next Steps

Key Points of This Article

1. Importance of Text Mining

• As a technique for extracting valuable information from text data, it is used across a wide range of fields such as business, research, and healthcare.

1. Suitability of Python

• Python, with its rich libraries and ease of use, is a platform that allows even beginners to start text mining easily.

1. Environment Setup and Practical Methods

• Installation of required libraries (such as pandas, MeCab, wordcloud, etc.).
• We explained each technique—including data preprocessing, morphological analysis, visualization, sentiment analysis, and topic modeling—along with code examples.

1. Various Application Examples

• We presented examples such as analyzing product reviews, processing social media data, and applications in healthcare and education>

1. Resolving Questions in the FAQ

• We provided concrete solutions to challenges that beginners often encounter.

Next Steps

1. Try the Sample Code

• Run the code examples introduced in this article on Google Colab or your local environment.

1. Collect and Analyze Your Own Data

• Gather text data that interests you—such as social media posts or product reviews—and conduct hands‑on analysis.

1. Access Further Learning Resources

• Use the official Python documentation and data‑science books to deepen your knowledge.

1. Challenge Advanced Analyses

• Apply topic modeling and machine‑learning algorithms to learn techniques for extracting deeper insights from large datasets.