Aspect Detection in Sportswear Apparel Reviews for Opinion Mining

Rajapaksha, Sampath; Ranathunga, Surangika

doi:10.1109/mercon55799.2022.9906265

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…It is based on the FAST-LCF-BERT model with Microsoft/DeBERTa-v3-base, which comes from PyABSA [75], [74]. The model is trained with 30k+ ABSA samples and fine-tuned with 180k examples for the ABSA dataset including SemEval-2014 Task 4 (Laptop14 and Restaurant14) [25], SemEval-2016 Task 5(Restaurant-16) [49], MAMS [23], Television [12], and TShirt [50], [40]. However, the base model, LCF-BERT is an ABSA model that enhances sentiment polarity predictions by focusing more on local context words through the use of a Local Context Focus (LCF) mechanism [77].…”

Section: Fine-tuned Absa Modelsmentioning

confidence: 99%

“…This section discusses the results of ABSA models that are recently been proposed and fine-tuned on ABSA datasets. DeBERTa-v3-base-ABSA-v1.1 fine-tuned with 180k examples for the ABSA dataset including SemEval challenge of datasets form 2014 and 2016 (Laptop14 and Restaurant14, Restaurant-16) [25], [49], MAMS [23], Television [12], and TShirt [50], [40]. Overall, this model is mainly fine-tuned in the Restaurant, Laptop, Tshirt, and Television domains.…”

Section: B Performance Analysis Of Fine-tuned Absa Modelsmentioning

confidence: 99%

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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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Section: Fine-tuned Absa Modelsmentioning

confidence: 99%

Section: B Performance Analysis Of Fine-tuned Absa Modelsmentioning

confidence: 99%

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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“…In the realm of virtual reality, opinion mining takes on a new dimension, leveraging advanced algorithms and natural language processing techniques to discern and interpret the sentiments expressed within immersive digital environments [7]. The vast amount of usergenerated content, ranging from social media posts to virtual interactions within VR spaces, provides a rich source of data for opinion mining [8]. By employing sophisticated algorithms, machine learning models, and linguistic analysis, opinion mining in virtual reality seeks to uncover not only the surface-level sentiments but also the underlying emotions, attitudes, and opinions of individuals navigating these synthetic worlds [9].…”

Section: Introductionmentioning

confidence: 99%

Design of Hotel Room Experience Based on Virtual Reality Technology

Jun Wang

2024

jes

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Virtual reality (VR) technology within the hotel industry marks a transformative shift in the way guests experience and engage with hospitality services. Virtual reality, with its immersive and interactive capabilities, enables hotels to provide a novel and engaging environment for guests. From virtual tours of hotel rooms and amenities to immersive experiences showcasing local attractions and cultural highlights, VR has the potential to revolutionize the pre-booking and on-site guest experience. This paper focused on the user experiences within hotel rooms enhanced with virtual reality (VR) technology. Leveraging content analysis, sentiment analysis, and advanced classification models, we aim to unravel the intricacies of user sentiments and preferences in this evolving domain. The content analysis reveals a spectrum of user opinions, ranging from enthusiastic endorsements of immersive VR content to nuanced critiques of room ambiance and interactivity. Subsequently, a sentiment analysis model accurately categorizes these sentiments, showcasing its effectiveness in capturing the diverse user expressions. Our classification analysis demonstrates the robustness of the sentiment analysis model, with high accuracy, precision, recall, and F1-score metrics. Comparatively, we introduce a proposed BERT model, harnessing advanced natural language processing techniques, and observe its performance against traditional sentiment analysis and an AutoEncoder Model. The results indicate that the BERT model matches the performance of traditional sentiment analysis, outperforming the AutoEncoder Model. This underscores the effectiveness of leveraging state-of-the-art language models in understanding and classifying user sentiments.

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Topic Modeling for Mining Opinion Aspects from a Customer Feedback Corpus

Babina

2024

Autom. Doc. Math. Linguist.

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Aspect Detection in Sportswear Apparel Reviews for Opinion Mining

Cited by 3 publications

References 26 publications

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

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

Design of Hotel Room Experience Based on Virtual Reality Technology

Topic Modeling for Mining Opinion Aspects from a Customer Feedback Corpus

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