Artificial Intelligence
Atefeh Mohammadi; Mohammad Reza Pajoohan; Ali Mohammad Zareh Bidoki
Abstract
Background and Objectives: In Natural Language Processing (NLP), sentiment analysis is crucial for understanding and extracting aspects and opinions expressed in textual data. Recent methods have emphasized determining polarity in multi-domain sentiment analysis while giving less attention to aspect ...
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Background and Objectives: In Natural Language Processing (NLP), sentiment analysis is crucial for understanding and extracting aspects and opinions expressed in textual data. Recent methods have emphasized determining polarity in multi-domain sentiment analysis while giving less attention to aspect and opinion extraction. Furthermore, the terms that convey aspects and opinions may have different importance in different domains, and this difference should be considered to enhance the extraction of aspect-opinion pairs. Methods: To address these challenges, we propose a Weighted Words Multi-Domain (WWMD) model for aspect-opinion pairs extraction, consisting of a self-attention mechanism and a dense network. The self-attention mechanism extracts each word's importance according to the sentence's overall meaning. The dense network is used for domain prediction. It assigns greater weight to words relevant to each domain, which leads to considering the different significance of terms across various contexts. Adding an attention mechanism to the domain module allows for a clearer understanding of different aspects and opinions across various domains. We utilize a two-channel approach, one channel extracts aspects and opinions, while the other extracts the relationships between them. The weighted words extracted by our model are simultaneously considered as the input for both channels.Results: Using weighted words specific to each domain, improves the model output.Conclusion: Evaluation results on benchmark datasets demonstrate the superiority of the proposed model compared to state-of-the-art techniques.
Artificial Intelligence
L. Hafezi; S. Zarifzadeh; M. R. Pajoohan
Abstract
Background and Objectives: Detecting multiple entities within financial texts and accurately analyzing the sentiment associated with each is a challenging yet critical task. Traditional models often struggle to capture the nuanced relationships between multiple entities, especially when sentiments are ...
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Background and Objectives: Detecting multiple entities within financial texts and accurately analyzing the sentiment associated with each is a challenging yet critical task. Traditional models often struggle to capture the nuanced relationships between multiple entities, especially when sentiments are context-dependent and spread across different levels of a document. Addressing these complexities requires advanced models that can not only identify multiple entities but also distinguish their individual sentiments within a broader context. This study aims to introduce and evaluate two novel methods, ENT-HAN and SNT-HAN, built upon the Hierarchical Attention Networks, specifically designed to enhance the accuracy of both entity extraction and sentiment analysis in complex financial documents.Methods: In this study, we design ENT-HAN and SNT-HAN methods to address the tasks of multi-entity detection and sentiment analysis within financial texts. The first method focuses on entity extraction, where capture hierarchical relationships between words and sentences. By utilizing word-level attention, the model identifies the most relevant tokens for recognizing entities, while sentence-level attention helps refine the context in which these entities appear, allowing the model to detect multiple entities with precision. The second method is applied for sentiment analysis, aiming to classify sentiments into positive, negative, or neutral categories. The sentiment analysis model employs hierarchical attention to identify the most important words and sentences that convey sentiment about each entity. This approach ensures that the model not only focuses on the overall sentiment of the text but also accounts for context-specific variations in sentiment across different entities. Both methods were evaluated on FinEntity dataset, and the results demonstrate their effectiveness, with significantly improving the accuracy of both entity extraction and sentiment classification tasks.Results: The ENT-HAN and SNT-HAN demonstrated strong performance in both entity extraction and sentiment analysis, outperforming the methods they were compared against. For entity extraction, ENT-HAN was evaluated against RNN and BERT models, showing superior accuracy in identifying multiple entities within complex texts. In sentiment analysis, SNT-HAN was compared to the best-performing method previously applied to FinEntity dataset. Despite the good performance of the existing methods, SNT-HAN demonstrated superior results, achieving a better accuracy.Conclusion: The outcome of this research highlights the potential of the ENT-HAN and SNT-HAN for improving entity extraction and sentiment analysis accuracy in financial documents. Their ability to model attention at multiple levels allows for a more nuanced understanding of text, establishing them as a valuable resource for complex tasks in financial text analysis.
