text: Textual analytics based on representations of documents as bags of words have been reasonably successful. However, analysis that requires deeper insight into language, into author properties, or into the contexts in which documents were created requires a richer representation. Systemic nets are one such representation. They have not been extensively used because they required human effort to construct. We show that systemic nets can be algorithmically inferred from corpora, that the resulting nets are plausible, and that they can provide practical benefits for knowledge discovery problems. This opens up a new class of practical analysis techniques for textual analytics.
  @cite_10: This chapter uses Systemic Functional Linguistic (SFL) theory as a basis for extracting semantic features of documents. We focus on the pronominal and determination system and the role it plays in constructing interpersonal distance. By using a hierarchical system model that represents the author’s language choices, it is possible to construct a richer and more informative feature representation with superior computational efficiency than the usual bag-of-words approach. Experiments within the context of financial scam classification show that these systemic features can create clear separation between registers with different interpersonal distance. This approach is generalizable to other aspects of attitude and affect that have been modelled within the systemic functional linguistic theory.
  @cite_1: 
  @cite_12: Recent years have seen a growing interest in non-topical text analysis, in which characterizations are sought of the opinions, feelings, and attitudes expressed in a text, rather than just the facts. A key problem in this area is sentiment classification, in which a document is labelled as a positive (‘thumbs up’) or negative (’thumbs down’) evaluation of a target object (film, book, product, etc.). Immediate applications include data and web mining, market research, and customer relationship management.
summary: There have been several applications of predefined systemic nets to textual prediction problems. For example, Whitelaw @cite_12 show improvement in sentiment analysis using the Appraisal Net mentioned above. Argamon show how to predict personality type from authored text, again using systemic functional ideas @cite_1 . Herke-Couchman and Patrick derive interpersonal distance from systemic network attributes @cite_10 .