T${}^2$K${}^2$: The Twitter Top-K Keywords Benchmark
This provides a standardized tool for researchers and practitioners in information retrieval to compare performance, but it is incremental as it addresses a specific gap rather than introducing a new method.
The paper tackles the lack of a benchmark for evaluating weighting schemes and database implementations in computing top-k keywords from textual data, by introducing T^2K^2, which uses a real tweet dataset and queries with varying complexities and selectivities, and demonstrates its relevance through tests on TF-IDF, Okapi BM25, and databases like Oracle, PostgreSQL, and MongoDB.
Information retrieval from textual data focuses on the construction of vocabularies that contain weighted term tuples. Such vocabularies can then be exploited by various text analysis algorithms to extract new knowledge, e.g., top-k keywords, top-k documents, etc. Top-k keywords are casually used for various purposes, are often computed on-the-fly, and thus must be efficiently computed. To compare competing weighting schemes and database implementations, benchmarking is customary. To the best of our knowledge, no benchmark currently addresses these problems. Hence, in this paper, we present a top-k keywords benchmark, T${}^2$K${}^2$, which features a real tweet dataset and queries with various complexities and selectivities. T${}^2$K${}^2$ helps evaluate weighting schemes and database implementations in terms of computing performance. To illustrate T${}^2$K${}^2$'s relevance and genericity, we successfully performed tests on the TF-IDF and Okapi BM25 weighting schemes, on one hand, and on different relational (Oracle, PostgreSQL) and document-oriented (MongoDB) database implementations, on the other hand.