Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis (1911.01616v4)

Abstract: Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from "Waiters are very friendly and the pasta is simply average" could be ('Waiters', positive, 'friendly'). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.

A Comprehensive Approach to Aspect-based Sentiment Analysis: The ASTE Framework

In the domain of aspect-based sentiment analysis (ABSA), the paper titled "Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis" introduces a significant advancement by proposing the Aspect Sentiment Triplet Extraction (ASTE) task. This task endeavors to integrate the otherwise fragmented tasks of ABSA into a cohesive solution by extracting complete sentiment triplets that address the questions of what the target aspect is, how its sentiment is polarized, and why it is perceived that way. The methodologies and results presented in this paper offer contributions towards improving the granularity and applicability of sentiment analysis in real-world applications.

Technical Contributions and Methodology

The paper establishes a novel two-stage framework designed to efficiently handle the ASTE task. The first stage is focused on predicting three main components: the aspects, the corresponding sentiment polarities, and the opinions which act as explanatory elements tied to the sentiments. This is achieved through a systematic process of sequence labeling, guided by a unified tagging system for aspects and a BIO-like tagging for opinion extraction. The integration of stacked Bidirectional Long Short-Term Memory (BLSTM) networks, combined with Graph Convolutional Networks (GCN), allows the framework to harness both semantic sequence patterns and syntactic dependency relations to enrich the extraction process.

The second stage aims to consolidate the outputs from the first stage by pairing related aspects with opinions to form sentiment triplets. This pairing process relies heavily on the relative word distance between predicted aspects and opinions, utilizing position embeddings to guide a BLSTM encoder in making accurate pair classifications.

Experimental Results and Implications

The empirical results demonstrate the efficacy of the proposed framework, as it surpasses baseline models across multiple benchmark datasets such as Semeval's 14res, 14lap, 15res, and 16res. Notably, the model exhibits strong precision and recall in both aspect and opinion extraction, signaling a thorough understanding of the context and semantics inherent in textual data.

Furthermore, the ablation studies assert the soundness of each component within the framework, indicating significant contributions from the incorporation of opinion term extraction and the mutual interplay among the modeled tasks. The paper addresses critical challenges in ABSA by proposing this end-to-end approach, offering a more refined and complete sentiment analysis mechanism that could serve as a cornerstone for future research and applications involving nuanced sentiment evaluation.

Theoretical and Practical Implications

From a theoretical standpoint, this research advances the understanding of integrated task processing within sentiment analysis. It posits that the mutual reinforcement between aspect and opinion extraction can yield more comprehensive sentiment mapping—a finding that could influence subsequent models in sentiment analysis and related domains.

Practically, the ASTE framework opens avenues for applications where understanding sentiment causality is crucial, such as customer feedback systems, social media monitoring tools, and other sentiment-driven applications across various industries. The ability to extract rich sentiment triplets not only promises to enhance the quality of insights but also tailors the sentiment analysis process to meet specific user needs more effectively.

Future Directions

The promising results showcased by the ASTE framework suggest numerous directions for further exploration. Future research may delve into refining the deep learning architectures used, potentially exploring transformer-based models to improve context understanding further. Additionally, addressing the scalability and adaptability of the ASTE framework to low-resource languages or cross-domain scenarios presents valuable areas for extending this research.

In conclusion, this paper proposes a near-complete solution for ABSA, strongly substantiated by its innovative approach and robust experimental validation. The practicality and comprehensiveness offered by the ASTE framework mark a significant stride towards more meaningful and context-aware sentiment analysis.