A critical empirical evaluation of deep learning models for solving aspect based sentiment analysis

Dhanith, P. R. Joe; Prabha, K. Arun

doi:10.1007/s10462-023-10460-0

Cited by 4 publications

(1 citation statement)

References 52 publications

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“…With the rapid growth of the text data amount, there is an urgent need for developing techniques that can automatically represent and process features. Therefore, deep learning methods have been widely applied in aspect-based sentiment analysis tasks and achieved remarkable progress [7]. Deep learning-based text sentiment analysis approaches can map initially discrete text data to real-valued vector spaces.…”

Section: Introductionmentioning

confidence: 99%

Enhanced heterogeneous graph convolutional networks with dual-level attention for aspect-based sentiment analysis

Zou¹,

Wang

2023

Preprint

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Aspect-based sentiment analysis aims to analyze the sentimental tendencies of specific aspect terms in the target sentence. With the continuous development of deep learning technology, graph convolutional networks have been widely applied to sentiment analysis tasks and achieved satisfactory results. However, when constructing graph convolutional networks based on text contents, the model considers the contextual words and their interdependent relationships without distinction. Meanwhile, most models are built separately, without fully employing the information of part of speech and dependency relationships in the text dependency parsing. In response to the above issues, we design a heterogeneous graph neural networks model based on dual node-level and type-level attention mechanisms. The heterogeneous information network is embedded into graph convolutional networks for considering the attribute characteristics of different node types. The dual-level attention mechanism can capture both the importance between diverse adjacent nodes and the significance of different node types to the central node. The relationship between aspect terms and sentiment terms is acquired by adjusting the weights of different types of nodes. To address the issue that graph neural networks have difficulty in fully utilizing dependency parsing information, we embedded syntactic and semantic dependency enhancement. By introducing the dependency tree and the dependency parsing into the graph convolutional networks architecture, it can comprehensively model the syntax relationship and analyze the sentence structure, thereby distinguishing the important contextual information. The experimental results on multiple public datasets indicate the effectiveness of the proposed model in aspect-based sentiment analysis.

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Section: Introductionmentioning

confidence: 99%

Enhanced heterogeneous graph convolutional networks with dual-level attention for aspect-based sentiment analysis

Zou¹,

Wang

2023

Preprint

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A Sparse Self-Attention Enhanced Model for Aspect-Level Sentiment Classification

Dhanith,

Surendiran,

Rohith

et al. 2024

Neural Process Lett

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Aspect based sentiment analysis (ABSA) manifests the well refined work as Aspect-level sentiment classification (ASC) due to recent high attention and profound outcomes. This paper reveals the exorbitant relation of concerned aspect in determining the sentiment polarity of a sentence in addition to their content. Considering an instance, “The food is tasty but restaurant is untidy”, aspect food reveals the polarity as positive, while in case of restaurant, the polarity seems negative. Hence to scout relation between an $$aspect$$ aspect and $$context$$ context words present in sentence is much vital. In spite of the exceptional progress, the ASC experiences certain pitfalls as (1) The current attention based methods makes the given aspect to falsely relate syntactically unrelated words as related ones. (2) Single context-independent representation is only achieved by traditional Word2Vec or GloVe based embedding vectors. (3) Sentiments for multiple words that are inconsecutive remain insufficient for CNN based models. A Sparse-Self-Attention-based Gated Recurrent Unit with Aspect Embedding (SSA-GRU-AE) implementing BERT for ASC is proposed to solve these issues. The proposed SSA-GRU-AE mechanism is centralized on various portions of sentence as multiple aspects are taken as input. The experimental analysis on ASC datasets has proved that proposed model enhanced performance on ASC.

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Comparative Analysis of Deep Natural Networks and Large Language Models for Aspect-Based Sentiment Analysis

Mughal,

Mujtaba,

Shaikh

et al. 2024

IEEE Access

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Sentiment analysis is essential for comprehending public opinion, particularly when considering e-commerce and the expansion of online businesses. Early approaches treated sentiment analysis as a document or sentence-level classification problem, lacking the ability to capture nuanced opinions about specific aspects. This limitation was addressed by the development of aspect-based sentiment analysis (ABSA), which links sentiment to specific aspects that are mentioned explicitly or implicitly in the review. ABSA is relatively a new field of sentiment analysis and the existing models for ABSA face three main challenges, including domain-specificity, reliance on labeled data, and a lack of exploration into the potential of newer large language models (LLMs) such as GPT, PaLM, and T5. Leveraging a diverse set of datasets, including DOTSA, MAMS, and SemEval16, we evaluate the performance of prominent models such as ATAE-LSTM, flan-t5-large-absa, Deberta, PaLM, and GPT-3.5-Turbo. Our findings reveal nuanced strengths and weaknesses of these models across different domains, with Deberta emerging as consistently high-performing and PaLM demonstrating remarkable competitiveness for aspect term sentiment analysis (ATSA) tasks. In addition, the PaLM demonstrates competitive performance for all the domains that were used in the experiments including the restaurant, hotel, books, clothing, and movie reviews. Notably, the analysis underscores the models' domain sensitivity, shedding light on their varying efficacy for both ATSA and ACSA tasks. These insights contribute to a deeper understanding of model applicability and highlight potential areas for improvement in ABSA research and development.INDEX TERMS aspect-based sentiment analysis (ABSA), large language model (LLM), GPT, PaLM, BERT

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A critical empirical evaluation of deep learning models for solving aspect based sentiment analysis

Cited by 4 publications

References 52 publications

Enhanced heterogeneous graph convolutional networks with dual-level attention for aspect-based sentiment analysis

Enhanced heterogeneous graph convolutional networks with dual-level attention for aspect-based sentiment analysis

A Sparse Self-Attention Enhanced Model for Aspect-Level Sentiment Classification

Comparative Analysis of Deep Natural Networks and Large Language Models for Aspect-Based Sentiment Analysis

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