Aspect Based Sentiment Analysis with Gated Convolutional Networks (1805.07043v1)

Abstract: Aspect based sentiment analysis (ABSA) can provide more detailed information than general sentiment analysis, because it aims to predict the sentiment polarities of the given aspects or entities in text. We summarize previous approaches into two subtasks: aspect-category sentiment analysis (ACSA) and aspect-term sentiment analysis (ATSA). Most previous approaches employ long short-term memory and attention mechanisms to predict the sentiment polarity of the concerned targets, which are often complicated and need more training time. We propose a model based on convolutional neural networks and gating mechanisms, which is more accurate and efficient. First, the novel Gated Tanh-ReLU Units can selectively output the sentiment features according to the given aspect or entity. The architecture is much simpler than attention layer used in the existing models. Second, the computations of our model could be easily parallelized during training, because convolutional layers do not have time dependency as in LSTM layers, and gating units also work independently. The experiments on SemEval datasets demonstrate the efficiency and effectiveness of our models.

• The paper’s main contribution is a novel gated CNN model that employs Gated Tanh-ReLU Units to selectively extract aspect-specific sentiment features.
• The model leverages parallelizable convolutional layers to reduce training time compared to traditional LSTM-based approaches.
• Experimental results on SemEval datasets demonstrate improved accuracy and efficiency, making it promising for real-time sentiment analysis applications.

Aspect-Based Sentiment Analysis with Gated Convolutional Networks

The paper "Aspect Based Sentiment Analysis with Gated Convolutional Networks" by Wei Xue and Tao Li explores a novel approach to fine-grained sentiment analysis, focusing on aspect-based sentiment analysis (ABSA). This task involves predicting sentiment polarity in relation to specific entities or aspects within a text, providing more detailed insights than generic sentiment analysis.

Background and Motivation

Aspect-based sentiment analysis is typically divided into two subtasks: aspect-category sentiment analysis (ACSA) and aspect-term sentiment analysis (ATSA). ACSA deals with predefined aspect categories, while ATSA targets specific terms or entities within the text. Traditionally, models leveraging LSTMs and attention mechanisms have dominated this area. However, these approaches often suffer from computational complexity and slower training times due to their inherent sequential processing and intensive attention mechanisms.

Proposed Model: Gated Convolutional Networks

The authors introduce a model based on gated convolutional networks that aim to address the limitations of LSTM-based approaches. Key innovations include:

• Gated Tanh-ReLU Units (GTRU): These units selectively filter and output sentiment features relevant to the given aspect or entity, simplifying the architecture compared to attention layers.
• Easy Parallelization: The convolutional layers in the model eliminate time dependency, enabling efficient parallel computations during training.

This methodology promises faster training with minimal loss in accuracy, potentially outperforming traditional models such as ATAE-LSTM and RAM.

Experimental Results

The model was tested on SemEval datasets, focusing on review datasets from restaurants and laptops. Several observations stand out:

• Accuracy: GCAE exhibits improved accuracy over previous models, such as ATAE-LSTM, particularly on datasets designed to challenge models with reviews that have conflicting sentiments for different aspects.
• Efficiency: Computational efficiency is a haLLMark of this model, achieving lower training times when compared to LSTM and attention-based models.

Implications and Future Work

This research offers a promising direction for ABSA, especially in environments where computational resources are constrained. Gated convolutional networks could significantly enhance real-time sentiment analysis applications by reducing processing overhead.

The paper also hints at the potential integration of sentiment lexicons into neural networks, a topic warranting further exploration. Incorporating large-scale lexicon data could enhance the depth and accuracy of sentiment analysis while retaining the computational efficiency demonstrated.

Conclusion

The authors have contributed a compelling alternative to LSTM-based models in aspect-based sentiment analysis, showcasing both improved efficiency and comparable accuracy. By leveraging gated convolutional networks, this research marks a step forward in handling nuanced sentiment data with reduced computational demands. Further development could focus on integrating lexicon resources, enhancing the model’s capability to interpret complex sentiment nuances.