Create Stunning Word Clouds from WeChat Moments Using Python and Jieba

We previously scraped WeChat Moments data using a Python web crawler; now we will visualize the text with a word cloud.

Step 1: Install the wordcloud and jieba libraries via pip install wordcloud jieba.

Step 2: Download a Chinese font such as simhei.ttf for rendering Chinese characters in the cloud.

Step 3: Create analyse.py alongside items.py and define analyse_words. Load the scraped moment.json file (ensure encoding='utf-8' to avoid GBK errors) and use jieba.cut to obtain word segmentation.

Step 4: Run the program; it generates keys.png in the items.py directory.

Step 5: Open keys.png to view the initial word‑cloud result.

Step 6: The default cloud looks cluttered; improve its appearance using the online tool WordArt.

Step 7: Try different shapes, such as an animal silhouette, for a more attractive visual.

Step 8: To avoid overly dense text, preprocess the JSON data: extract all text into moment.txt.

Step 9: Perform word segmentation on the extracted text.

Step 10: The segmentation output is saved to moment_outputs.txt, showing the token list.

Step 11: Count word frequencies and write the results to a text and Excel file.

Step 12: The frequency table is displayed without errors.

Step 13: Import the keyword‑frequency list into WordArt for final visualization.

Step 14: Adjust shape, font, layout, and colors to produce a colorful cloud, e.g., a dog‑shaped word cloud.

Step 15: Create additional designs such as a cloud‑shaped word cloud for a visual feast.

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