Synonymy　and　polysemy　present　a　challenge　to　effective　natural　language　processing,　especially　in　the　situations　of　context　absence　and　sparse　feature　in　short　texts,　widened　semantic　gap　between　low-level　text　features　representation　and　high-level　interpretation.　In　this　work,　short　texts　were　reorganized　into　special　context,　i.e.,　the　implied　internal　relationship　such　as　time　and　space,　and　a　novel　two-step　scheme　for　semantic　orientation　detection　based　on　the　special　context　was　proposed.　In　the　first　step,　the　short　texts　were　reorganized　into　special　contexts　by　the　implied　internal　relationship.　In　the　second　step,　the　unknown　short　text　was　categorized　into　a　special　context　and　labeled　a　polarity　tag　using　the　inner　semantic　orientation　classifier.　We　firstly　discussed　the　effect　of　special　context　after　a　sentiment　classification　framework　based　on　naive　Bayes　classifier　was　presented.　Then　an　enhancement　classification　method　was　given　using　field　concept,　which　was　expanded　to　special　context.　Finally,　a　special　context　reorganizing　method　was　proposed　based　on　genetic　algorithm.　Theoretical　analysis　shows　the　proposed　methods　can　reduce　the　sample　error　and　approximation　error　under　some　constraints.　The　experimental　results　in　real　corpora　show　the　effectiveness　of　the　proposed　method.　Copyright　©　2012　Acta　Automatica　Sinica.　All　rights　reserved.