The most widely used similarity measure in the field of natural language processing may be cosine similarity. However, in the context of Twitter, the large scale of massive tweet data inevitably makes it expensive to perform cosine similarity computations among tremendous data samples. In this paper, we exploit binary coding to tackle the scalability issue, which compresses each data sample into a compact binary code and hence enables highly efficient similarity computations via Hamming distances between the generated codes. In order to yield semantics sensitive binary codes for tweet data, we design a binarized matrix factorization model and further improve it in two aspects. First, we force the projection directions employed by the model nearly orthogonal to reduce the redundant information in their resulting binary bits. Second, we leverage the tweets' neighborhood information to encourage similar tweets to have adjacent binary codes. Evaluated on a tweet dataset using hashtags to create gold labels in an information retrieval scenario, our proposed model shows significant performance gains over competing methods.
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