Sentiment Analysis using Word2vec-CNN-BiLSTM Classification
Yue Wang, Lei Li
Abstract
Traditional neural network based short text classification algorithms for sentiment classification is easy to find the errors. In order to solve this problem, the Word Vector Model (Word2vec), Bidirectional Long-term and Short-term Memory networks (BiLSTM) and convolutional neural network (CNN) are combined. The experiment shows that the accuracy of CNN-BiLSTM model associated with Word2vec word embedding achieved 91.48%. This proves that the hybrid network model performs better than the single structure neural network in short text.
Topics & Concepts
Word2vecComputer scienceArtificial intelligenceConvolutional neural networkWord embeddingWord (group theory)Artificial neural networkTerm (time)EmbeddingSentiment analysisPattern recognition (psychology)Machine learningMathematicsPhysicsGeometryQuantum mechanicsSentiment Analysis and Opinion MiningTopic ModelingAdvanced Text Analysis Techniques