Lost in Translation: How L33tspeak Might Throw Sentiment Analysis Models for a Loop

This blog examines how adversarial text via l33tspeak affects sentiment analysis models, demonstrating that obfuscating key sentiment-bearing tokens can shift classifications—English DistilBERT was relatively robust, while several Spanish-language models showed greater susceptibility. Using HuggingFace pipelines, the author compares outputs across models and languages, attributing differences to training data size, composition, and exposure to l33tspeak. The post highlights potential misuse for evading monitoring (e.g., brand sentiment tools, spam filters) and calls for broader testing across languages, better documentation of training datasets, and analysis of l33tspeak prevalence to explain model weaknesses.