The role of pragmatics for grammatical change: the case of preverbal non

What is the role of pragmatics in the evolution of grammatical paradigms? It is to maintain marked candidates that may come to be the default expression. This perspective is validated by the Jespersen cycle, where the standard expression of sentential negation is renewed as pragmatically marked negatives achieve default status. How status changes are effected, however, remains to be documented. This is what is achieved in this paper that looks at the evolution of preverbal negative non in Old and Middle French. The negative, which categorically marks pragmatic activation (Dryer 1996) with finite verbs in Old French, loses this value when used with non-finite verbs in Middle French. This process is accompanied by competing semantic reanalyses of the distribution of infinitives negated in this way, and by the co-occurrence with a greater lexical variety of verbs. The absence of pragmatic contribution should lead the marker to take on the role of default, which is already fulfilled by a well-established ne ... pas, pushing non to decline. Hard empirical evidence is thus provided that validates the assumed role of pragmatics in the Jespersen cycle, supporting the general view of pragmatics as supporting alternative candidates that may or may not achieve default status in the evolution of a grammatical paradigm.

SentiCircles for contextual and conceptual semantic sentiment analysis of Twitter

Lexicon-based approaches to Twitter sentiment analysis are gaining much popularity due to their simplicity, domain independence, and relatively good performance. These approaches rely on sentiment lexicons, where a collection of words are marked with fixed sentiment polarities. However, words' sentiment orientation (positive, neural, negative) and/or sentiment strengths could change depending on context and targeted entities. In this paper we present SentiCircle; a novel lexicon-based approach that takes into account the contextual and conceptual semantics of words when calculating their sentiment orientation and strength in Twitter. We evaluate our approach on three Twitter datasets using three different sentiment lexicons. Results show that our approach significantly outperforms two lexicon baselines. Results are competitive but inconclusive when comparing to state-of-art SentiStrength, and vary from one dataset to another. SentiCircle outperforms SentiStrength in accuracy on average, but falls marginally behind in F-measure. © 2014 Springer International Publishing.