Computational syntactic processing is a fundamental technique in natural language processing. It normally serves as a pre-processing method to transform natural language into structured and normalized texts, yielding syntactic features for downstream task learning. In this work, we propose a systematic survey of low-level syntactic processing techniques, namely: microtext normalization, sentence boundary disambiguation, part-of-speech tagging, text chunking, and lemmatization. We summarize and categorize widely used methods in the aforementioned syntactic analysis tasks, investigate the challenges, and yield possible research directions to overcome the challenges in future work.