how to effectively normalize tf-idf scores for short-form content in semantic analysis, avoiding data sparsity issues?

so, our current tf-idf implementation does a pretty solid job for long-form content, like blog posts or articles, when we're trying to figure out keyword relevance and document similarity. but man, when we try to apply that to really short texts, like product descriptions, tweets, or even just snippet analysis, it completely falls apart. it's like, you know, the standard normalization methods we're using, things like cosine similarity or just a basic L2 norm, they totally over-inflate the importance of rare terms in those short-form pieces.

this leads to massively skewed results in our semantic analysis, making it really hard to accurately gauge the actual topic or find true document similarity. we're constantly running into data sparsity issues because the vocabulary in short texts is so limited, and these normalization techniques just amplify that noise instead of reducing it. i'm tryna find some robust strategies or maybe even some alternative normalization functions that are specifically designed for short, concise content to prevent these data sparsity problems and give us more meaningful semantic insights