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Tweet-level sentiment classification in Twitter social networking has many challenges: exploiting syntax, semantic, sentiment and context in tweets. To address these problems, we propose a novel approach to sentiment analysis that uses lexicon features for building lexicon embeddings (LexW2Vs) and generates character attention vectors (Char-AVs) by using a Deep Convolutional Neural Network (DeepCNN). Our approach integrates LexW2Vs and CharAVs with continuous word embeddings (Continu-ousW2Vs)dblp:conf/ijcnlp/NguyenN17 fatcat:65duaogigndj3ek4foalca6ojq