Emotion-Cause Pair Extraction in Customer Reviews

TL;DR

This paper presents a neural network-based approach for extracting emotion-cause pairs from online customer reviews, utilizing emotion-aware embeddings and a Bi-LSTM to identify emotionally relevant clauses, with promising results on a manually annotated dataset.

Contribution

It introduces a novel ECPE pipeline tailored for online reviews, combining emotion-aware embeddings with neural network models to improve extraction accuracy.

Findings

Achieved promising extraction results on a limited dataset

Developed a domain-specific ECPE algorithm for reviews

Integrated emotion-aware embeddings into the extraction process

Abstract

Emotion-Cause Pair Extraction (ECPE) is a complex yet popular area in Natural Language Processing due to its importance and potential applications in various domains. In this report , we aim to present our work in ECPE in the domain of online reviews. With a manually annotated dataset, we explore an algorithm to extract emotion cause pairs using a neural network. In addition, we propose a model using previous reference materials and combining emotion-cause pair extraction with research in the domain of emotion-aware word embeddings, where we send these embeddings into a Bi-LSTM layer which gives us the emotionally relevant clauses. With the constraint of a limited dataset, we achieved . The overall scope of our report comprises of a comprehensive literature review, implementation of referenced methods for dataset construction and initial model training, and modifying previous work in ECPE by proposing an improvement to the pipeline, as well as algorithm development and implementation for the specific domain of reviews.