In this paper, we study the use of data mining techniques for stylistic analysis, from a linguistic point of view, by considering emerging sequential patterns. First, we show that mining sequential patterns of words with gap constraints gives new relevant linguistic patterns with respect to patterns built on state-of-the-art n-grams. Then, we investigate how sequential patterns of itemsets can provide more generic linguistic patterns. We validate our approach both from a quantitative and a linguistic point of view by conducting experiments on three corpora of various types of French texts (poetry, letters, and fiction, respectively). By considering more particularly poetic texts, we show that characteristic linguistic patterns can be identified using data mining techniques