The removal of stopwords is an important preprocessing step in many natural language processing tasks, which can lead to enhanced performance and execution time. Many existing methods either rely on a predefined list of stopwords or compute word significance based on metrics such as tf-idf. The objective of our work in this paper is to identify stopwords, in an unsupervised way, for streaming textual corpora such as Twitter, which have a temporal nature. We propose to consider and model the dynamics of a word within the streaming corpus to identify the ones that are less likely to be informative or discriminative. Our work is based on the discrete wavelet transform (DWT) of word signals in order to extract two features, namely scale and energy. We show that our proposed approach is effective in identifying stopwords and improves the quality of topics in the task of topic detection.
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