Why does sentiment analysis perform poorly on Twitter data containing significant 'netspeak'?

💡 Explanation

Sentiment analysis suffers when applied to texts with extensive 'netspeak' because the orthographic and lexical variations disrupt standard semantic mappings. The algorithms struggle because they are trained on conventional language, therefore netspeak expressions lead to incorrect sentiment classification, rather than purely syntactic errors or character-count effects.

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