简介：情绪分类上的State-of-the-arts研究典型地是域依赖者并且限制域。在这份报纸，我们试图减少领域相关性并且由建议一个有效多域情绪分类算法同时改进全面性能。我们的方法采用多重分类器联合的途径。在这条途径，我们首先与领域独立训练单个领域分类器特定的数据，然后为最后的决定联合分类器。我们的实验比两个挑选领域分类途径的证明这条途径更好表现(个别地使用训练数据)并且混合领域分类途径(都简单地联合训练数据)。特别地，有加权的和统治的分类器联合在单个领域分类上获得27.6%的平均错误减小。

简介：Sentimentanalysis,ahotresearchtopic,presentsnewchallengesforunderstandingusers＇opinionsandjudg-mentsexpressedonline.Theyaimtoclassifythesubjectivetextsbyassigningthemapolaritylabel.Inthispaper,weintroduceanovelmachinelearningframeworkusingauto-encodersnetworktopredictthesentimentpolaritylabelatthewordlevelandthesentencelevel.Inspiredbythedimensionalityreductionandthefeatureextractioncapabilitiesoftheauto-encoders,weproposeanewmodelfordistributedwordvectorrepresentation＂PMI-SA＂usingasinputpointwise-mutual-information＂PMI＂wordvectors.Theresultedcontinuouswordvectorsarecombinedtorepresentasentence.Anunsupervisedsentenceembeddingmethod,calledContextualRecursiveAuto-Encoders＂CoRAE＂,isalsodevelopedforlearningsentencerepresentation.Indeed,CoRAEfollowsthebasicideaoftherecursiveauto-encoderstodeeplycomposethevectorsofwordsconstitutingthesentence,butwithoutrelyingonanysyntacticparsetree.TheCoRAEmodelconsistsincombiningrecursivelyeachwordwithitscontextwords（neighbors＇words：previousandnext）byconsideringthewordorder.Asupportvectormachineclassifierwithfine-tuningtechniqueisalsousedtoshowthatourdeepcompositionalrepresentationmodelCoRAEimprovessignificantlytheaccuracyofsentimentanalysistask.Experimentalresultsdemon-stratethatCoRAEremarkablyoutperformsseveralcompetitivebaselinemethodsontwodatabases,namely,SanderstwittercorpusandFacebookcommentscorpus.TheCoRAEmodelachievesanefficiencyof83.28%withtheFacebookdatasetand97.57%withtheSandersdataset.

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