Lexicon Integrated CNN Models with Attention for Sentiment Analysis

Abstract

With the advent of word embeddings, lexicons are no longer fully utilized for sentiment analysis although they still provide important features in the traditional setting. This paper introduces a novel approach to sentiment analysis that integrates lexicon embeddings and an attention mechanism into Convolutional Neural Networks. Our approach performs separate convolutions for word and lexicon embeddings and provides a global view of the document using attention. Our models are experimented on both the SemEval'16 Task 4 dataset and the Stanford Sentiment Treebank, and show comparative or better results against the existing state-of-the-art systems. Our analysis shows that lexicon embeddings allow to build high-performing models with much smaller word embeddings, and the attention mechanism effectively dims out noisy words for sentiment analysis.

arXiv commentIn Proceedings of the EMNLP Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, of WASSA'17, 2017

References in corpus (4)

• Convolutional Neural Networks for Sentence Classification
• Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
• ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering
• Deep Networks with Internal Selective Attention through Feedback Connections

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• Hybrid Tiled Convolutional Neural Networks for Text Sentiment Classification
• Post-Processing of Word Representations via Variance Normalization and Dynamic Embedding
• Attention Incorporate Network: A network can adapt various data size