Topic Detection or topic modeling is a process of finding topics in a collection of textual data. Detecting topic for a very large document collection hardly done manually. Therefore, we need an automatic method, one of which is a clustering-based method such as fuzzy c-means (FCM). The standard initialization method of FCM is a random initialization which usually produces different topics for each execution. In this paper, we examine a nonrandom initialization method called nonnegative double singular value decomposition (NNDSVD). Besides the advantage of non-randomness, our simulations show that the NNDSVD method gives better accuracies in term of topic recall than both random method and another existing singular value decomposition-based method for the problem of sensing trending topic on Twitter.
|Number of pages||17|
|Journal||International Journal of Advances in Soft Computing and its Applications|
|Publication status||Published - 1 Jan 2018|
- Fuzzy c-means
- Singular value decomposition
- Topic detection
- Topic modeling