A Unified Model for Opinion Target Extraction and Target Sentiment Prediction (1811.05082v2)

Abstract: Target-based sentiment analysis involves opinion target extraction and target sentiment classification. However, most of the existing works usually studied one of these two sub-tasks alone, which hinders their practical use. This paper aims to solve the complete task of target-based sentiment analysis in an end-to-end fashion, and presents a novel unified model which applies a unified tagging scheme. Our framework involves two stacked recurrent neural networks: The upper one predicts the unified tags to produce the final output results of the primary target-based sentiment analysis; The lower one performs an auxiliary target boundary prediction aiming at guiding the upper network to improve the performance of the primary task. To explore the inter-task dependency, we propose to explicitly model the constrained transitions from target boundaries to target sentiment polarities. We also propose to maintain the sentiment consistency within an opinion target via a gate mechanism which models the relation between the features for the current word and the previous word. We conduct extensive experiments on three benchmark datasets and our framework achieves consistently superior results.

• The paper introduces a novel unified model that combines target extraction and sentiment classification using stacked recurrent neural networks.
• It employs a boundary guidance component with a transition matrix to effectively align target boundaries with sentiment polarities.
• Empirical results on benchmark datasets show robust performance and significant improvements over existing baselines.

A Unified Model for Opinion Target Extraction and Target Sentiment Prediction

The paper "A Unified Model for Opinion Target Extraction and Target Sentiment Prediction" presents a comprehensive approach to the task of Target-Based Sentiment Analysis (TBSA), focusing on unifying the processes of opinion target extraction and target sentiment classification. Traditionally, these subtasks have been addressed separately, which limits their practical use. The paper introduces a novel model that aims to solve TBSA in an end-to-end fashion using a unified tagging scheme.

Structural Overview

The proposed framework consists of two stacked recurrent neural networks (RNNs). The lower RNN focuses on auxiliary target boundary prediction, while the upper RNN predicts the unified tags for TBSA, effectively leveraging the boundary information from the auxiliary task to improve target sentiment classification. To achieve this, the authors introduce a transition matrix that encodes the dependency between target boundaries and sentiment polarities, facilitating the alignment of model predictions across the two tasks.

Technical Components

The model incorporates several key components to enhance performance:

1. Boundary Guidance (BG) Component: This leverages boundary predictions from the auxiliary task to guide the sentiment classification process in the upper RNN. It involves mapping the probability distribution of boundary predictions to the unified tag space.
2. Sentiment Consistency (SC) Component: A gate mechanism is introduced to maintain consistency in sentiment prediction within a multi-word opinion target. This component uses features from both the current and previous words to ensure coherent sentiment tags.
3. Opinion-Enhanced (OE) Target Word Detection Component: This auxiliary component enhances boundary prediction reliability by classifying words as potential target words based on their proximity to known opinion words.

Empirical Validation

Experimental validation on three benchmark datasets demonstrates the effectiveness of the proposed model, which consistently outperforms both existing baseline models and strong sequence taggers. The model particularly shows robustness in handling complex cases that involve intricate dependencies between target extraction and sentiment classification.

Implications and Future Directions

The proposed unified approach addresses the inherent inter-task dependencies in TBSA and provides an effective solution for jointly predicting opinion targets and their corresponding sentiments. Additionally, the auxiliary components ensure enhanced prediction accuracy and coherence across task boundaries. Future research could further explore the application of this unified model in various domains and languages, potentially considering more complex inter-task interactions and multimodal sentiment analysis scenarios. Additionally, integrating additional context-aware representations could further bolster model performance.

Overall, this paper contributes significantly to advancing methods in TBSA by presenting a well-structured model that brings together the two fundamental subtasks in a coherent and integrated manner.