With　the　rapid　growth　of　the　Web　text　data,　mining　and　analyzing　these　text　data,　especially　the　online　review　data　posted　by　the　users,　can　greatly　help　better　understand　the　usersconsuming　habits　and　public　opinions,　it　also　plays　an　important　role　in　decision-making　for　the　enterprises　and　the　government.　But　in　the　process　of　vectoring　text,　many　current　Chinese　text　sentiment　classifications　treat　words　as　atomic　units,　there　is　no　notion　of　similarity　between　words.　In　order　to　solve　this　problem,　this　paper　imports　word　embedding　to　capturing　both　the　semantic　and　syntactic　information　of　words　from　a　large　unlabeled　corpus.　In　the　section　of　experiment,　we　toke　the　noun,　verb,　and　adjectives　as　candidate　set,　used　chi(2)　statistic　to　reduce　the　number　of　dimensions.　We　mainly　compared　one-hot　representation　and　word　embedding　as　the　expression　of　word　to　certain　tasks,　we　also　proposed　the　pooling　method　with　word　embedding　to　standardizing　the　vector,　the　ELM　with　kernels　was　adopted　to　analyze　the　text　emotion　tendentiousness.　Finally　the　paper　summarizes　the　current　status,　remaining　challenges,　and　future　directions　in　the　field　of　sentiment　classification.