{"version":"1.0","type":"rich","title":"loading in LIWC","description":"The example shows how LIWC (Linguistic Inquiry and Word Count) dictionary words are loaded and assigned sentiment scores, distinguishing positive and negative emotion words from neutral function words. The Python script `speedy.py` reads a tab-separated file, checks each line for category markers like `125`, `126`, `127`, and `1`, and builds a dictionary `LIWC` mapping each word to a tuple of its score and a sequential index. It uses plain file iteration and string operations (`split`, `in`) without external libraries, incrementing a counter `i` for every accepted word.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-a1701809065b13f068c2?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}