2017 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)
Download PDF

Abstract

Sentiment analysis is a challenging task in Natural Language Processing due to the complexity of language structure, the semantic structure, and the relative scarcity of labeled data and context information especially in the field of short-text processing. To overcome data sparseness and the over-fitting problem when adopting a deep learning model, we propose multi-granularity text-oriented data augmentation technologies to generate large amounts of data for neural network training. We propose a novel confused model (LSCNN) with the proposed data argumentation technology that improves the performance and outperforms other effective neural network models. The proposed data augmentation method enhances the generalization ability of the proposed model. We also show that the proposed data augmentation method in combination with the neural networks model can achieve astonishing performance without any handcrafted features on cross-domain sentiment analysis, which is a efficient technology for comments sentiment detection.
Like what you’re reading?
Already a member?Sign In
Member Price
$11
Non-Member Price
$21
Add to CartSign In
Get this article FREE with a new membership!

Related Articles