Inheriting the Wisdom of Predecessors: A Multiplex Cascade Framework for Unified Aspect-based Sentiment Analysis

Goffredo, Pierpaolo; Haddadan, Shohreh; Vorakitphan, Vorakit; Cabrio, Elena; Villata, Serena

doi:10.24963/ijcai.2022/572

Cited by 26 publications

(9 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their methods can be applied to other artificial intelligence-based tasks. Other effective opinion mining methods include end-to-end neural-based methods, such as those in [25][26][27][28], as well as graph-based methods, such as those in [29][30][31][32].…”

Section: Arabic Dataset and Opinion Mining Methodsmentioning

confidence: 99%

User Opinion Prediction for Arabic Hotel Reviews Using Lexicons and Artificial Intelligence Techniques

Al-Mutawa

Al-Aama

2023

Applied Sciences

View full text Add to dashboard Cite

Opinion mining refers to the process that helps to identify and to classify users’ emotions and opinions from any source, such as an online review. Thus, opinion mining provides organizations with an insight into their reputation based on previous customers’ opinions regarding their services or products. Automating opinion mining in different languages is still an important topic of interest for scientists, including those using the Arabic language, especially since potential customers mostly do not rate their opinion explicitly. This study proposes an ensemble-based deep learning approach using fastText embeddings and the proposed Arabic emoji and emoticon opinion lexicon to predict user opinion. For testing purposes, the study uses the publicly available Arabic HARD dataset, which includes hotel reviews associated with ratings, starting from one to five. Then, by employing multiple Arabic resources, it experiments with different generated features from the HARD dataset by combining shallow learning with the proposed approach. To the best of our knowledge, this study is the first to create a lexicon that considers emojis and emoticons for its user opinion prediction. Therefore, it is mainly a helpful contribution to the literature related to opinion mining and emojis and emoticons lexicons. Compared to other studies found in the literature related to the five-star rating prediction using the HARD dataset, the accuracy of the prediction using the proposed approach reached an increase of 3.21% using the balanced HARD dataset and an increase of 2.17% using the unbalanced HARD dataset. The proposed work can support a new direction for automating the unrated Arabic opinions in social media, based on five rating levels, to provide potential stakeholders with a precise idea about a service or product quality, instead of spending much time reading other opinions to learn that information.

show abstract

Section: Arabic Dataset and Opinion Mining Methodsmentioning

confidence: 99%

User Opinion Prediction for Arabic Hotel Reviews Using Lexicons and Artificial Intelligence Techniques

Al-Mutawa

Al-Aama

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Aspect-based Sentiment Analysis Aspect-based sentiment analysis (ABSA) has become a pivotal research area within the field of affective computing (Picard 2000;Zhang et al 2023b;Fei et al 2020), with a significant foundation laid using the SemEval dataset (Pontiki et al 2014(Pontiki et al , 2015(Pontiki et al , 2016. Broadly, ABSA methodologies fall into two primary categories (Zhang et al 2023b;Fei et al 2022a). The first, single task, emphasizes individual components like aspect terms, opinion terms, categories, and sentiment polarities for an aspect.…”

Section: Related Workmentioning

confidence: 99%

Harnessing Holistic Discourse Features and Triadic Interaction for Sentiment Quadruple Extraction in Dialogues

Li,

Fei,

Liao

et al. 2024

AAAI

View full text Add to dashboard Cite

Dialogue Aspect-based Sentiment Quadruple (DiaASQ) is a newly-emergent task aiming to extract the sentiment quadruple (i.e., targets, aspects, opinions, and sentiments) from conversations. While showing promising performance, the prior DiaASQ approach unfortunately falls prey to the key crux of DiaASQ, including insufficient modeling of discourse features, and lacking quadruple extraction, which hinders further task improvement. To this end, we introduce a novel framework that not only capitalizes on comprehensive discourse feature modeling, but also captures the intrinsic interaction for optimal quadruple extraction. On the one hand, drawing upon multiple discourse features, our approach constructs a token-level heterogeneous graph and enhances token interactions through a heterogeneous attention network. We further propose a novel triadic scorer, strengthening weak token relations within a quadruple, thereby enhancing the cohesion of the quadruple extraction. Experimental results on the DiaASQ benchmark showcase that our model significantly outperforms existing baselines across both English and Chinese datasets. Our code is available at https://bit.ly/3v27pqA.

show abstract

“…Their research focused on the complexities of classifying multiple emotions within a single text, highlighting the intricate interplay between different emotional states. Furthering this exploration, Fei et al [30] introduced a multiplex cascade framework. This framework integrates various layers of sentiment analysis, offering a more comprehensive approach that benefits from the accumulated knowledge in the field.…”

Section: Related Work In the Literaturementioning

confidence: 99%

NLP Sentiment Analysis and Accounting Transparency: A New Era of Financial Record Keeping

Faccia,

McDonald,

George

2023

Computers

View full text Add to dashboard Cite

Transparency in financial reporting is crucial for maintaining trust in financial markets, yet fraudulent financial statements remain challenging to detect and prevent. This study introduces a novel approach to detecting financial statement fraud by applying sentiment analysis to analyse the textual data within financial reports. This research aims to identify patterns and anomalies that might indicate fraudulent activities by examining the language and sentiment expressed across multiple fiscal years. The study focuses on three companies known for financial statement fraud: Wirecard, Tesco, and Under Armour. Utilising Natural Language Processing (NLP) techniques, the research analyses polarity (positive or negative sentiment) and subjectivity (degree of personal opinion) within the financial statements, revealing intriguing patterns. Wirecard showed a consistent tone with a slight decrease in 2018, Tesco exhibited marked changes in the fraud year, and Under Armour presented subtler shifts during the fraud years. While the findings present promising trends, the study emphasises that sentiment analysis alone cannot definitively detect financial statement fraud. It provides insights into the tone and mood of the text but cannot reveal intentional deception or financial discrepancies. The results serve as supplementary information, enriching traditional financial analysis methods. This research contributes to the field by exploring the potential of sentiment analysis in financial fraud detection, offering a unique perspective that complements quantitative methods. It opens new avenues for investigation and underscores the need for an integrated, multidimensional approach to fraud detection.

show abstract

Inheriting the Wisdom of Predecessors: A Multiplex Cascade Framework for Unified Aspect-based Sentiment Analysis

Cited by 26 publications

References 5 publications

User Opinion Prediction for Arabic Hotel Reviews Using Lexicons and Artificial Intelligence Techniques

User Opinion Prediction for Arabic Hotel Reviews Using Lexicons and Artificial Intelligence Techniques

Harnessing Holistic Discourse Features and Triadic Interaction for Sentiment Quadruple Extraction in Dialogues

NLP Sentiment Analysis and Accounting Transparency: A New Era of Financial Record Keeping

Contact Info

Product

Resources

About