A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking
This work addresses sentiment analysis for social media users, but it is incremental as it builds on existing deep learning methods.
The paper tackles sentence-level sentiment classification on Twitter by combining a lexicon-based approach with a deep learning framework, achieving improved classification accuracy across three datasets.
This paper introduces a novel deep learning framework including a lexicon-based approach for sentence-level prediction of sentiment label distribution. We propose to first apply semantic rules and then use a Deep Convolutional Neural Network (DeepCNN) for character-level embeddings in order to increase information for word-level embedding. After that, a Bidirectional Long Short-Term Memory Network (Bi-LSTM) produces a sentence-wide feature representation from the word-level embedding. We evaluate our approach on three Twitter sentiment classification datasets. Experimental results show that our model can improve the classification accuracy of sentence-level sentiment analysis in Twitter social networking.