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Abstract. In this paper, a uniform approach for synthesizing monitors checking correctness properties specified in linear-time logics at runtime is provided. Therefore, a generic three-valued semantics is introduced reflecting the idea that prefixes of infinite computations are checked. Then a conceptual framework to synthesize monitors from a logical specification to check an execution incrementally is established, with special focus on resorting to the automata-theoretic approach. The merits of the presented framework are shown by providing monitor synthesis approaches for a variety of different logics such as LTL, the linear-time µ-calculus, PLTL mod , S1S, and RLTL.

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Purpose This paper aims to address the challenge of analysing the features of negative sentiment tweets. The method adopted in this paper elucidates the classification of social network documents and paves the way for sentiment analysis of tweets in further research. Design/methodology/approach This study classifies negative tweets and analyses their features. Findings Through negative tweet content analysis, tweets are divided into ten topics. Many related words and negative words were found. Some indicators of negative word use could reflect the degree to which users release negative emotions: part of speech, the density and frequency of negative words and negative word distribution. Furthermore, the distribution of negative words obeys Zipf’s law. Research limitations/implications This study manually analysed only a small sample of negative tweets. Practical implications The research explored how many categories of negative sentiment tweets there are on Twitter. Related words are helpful to construct an ontology of tweets, which helps people with information retrieval in a fixed research area. The analysis of extracted negative words determined the features of negative tweets, which is useful to detect the polarity of tweets by machine learning method. Originality/value The research provides an initial exploration of a negative document classification method and classifies the negative tweets into ten topics. By analysing the features of negative tweets, related words, negative words, the density of negative words, etc. are presented. This work is the first step to extend Plutchik’s emotion wheel theory into social media data analysis by constructing filed specific thesauri, referred to as local sentimental thesauri.