Sentiment　analysis　is　a　technology　with　great　practical　value,　it　can　solve　the　phenomenon　of　network　comment　information　disorderly　to　a　certain　extent,　and　accurate　positioning　of　user　information　required.　Currently　for　Chinese　sentiment　analysis　research　is　relatively　small,　including　a　variety　of　supervised　learning　method　of　classification　result　and　the　text　feature　representation　methods　and　feature　selection　mechanism　and　other　factors　impact　on　the　classification　performance　is　an　urgent　problem.　In　this　paper,　we　taken　the　verb,　adjectives　and　adverbs　as　text　features,　used　TF-IDF　to　calculate　weight　of　words.　Then　we　adopted　the　SVM　and　ELM　with　kernels　to　analyze　the　text　emotion　tendentiousness.　The　experimental　results　show　that　ELM　with　kernels　can　be　obtained　a　better　classification　result　in　a　relatively　short　period　of　time　than　SVM.　©　2016　IEEE.