Sentiment Analysis in E-Commerce Platforms: A Review of Current Techniques and Future Directions

Huang, Huang; Zavareh, Adeleh Asemi; Mustafa, Mumtaz Begum

doi:10.1109/access.2023.3307308

Cited by 18 publications

(2 citation statements)

References 66 publications

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“…The sentiment mining and analysis of text is a significant branch task of natural language analysis technology [3]. The task of e-commerce text emotions analysis is the analysis of text with emotional subjectivity [4], extraction of entities from valuable comments in the text, processing and summarization of them, and making reasonable inferences about the emotions expressed by reviewers. According to the granularity of the text processing, the task level of the emotions analysis is roughly classified into chapter fields, attribute fields, and sentence fields.…”

Section: Introductionmentioning

confidence: 99%

Chinese Multicategory Sentiment of E-Commerce Analysis Based on Deep Learning

Li,

Wang,

et al. 2023

Electronics

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With the continuous rise of information technology and social networks, and the explosive growth of network text information, text sentiment analysis technology now plays a vital role in public opinion monitoring and product development analysis on networks. Text data are high-dimensional and complex, and traditional binary classification can only classify sentiment from positive or negative aspects. This does not fully cover the various emotions of users, and, therefore, natural language semantic sentiment analysis has limitations. To solve this deficiency, we propose a new model for analyzing text sentiment that combines deep learning and the bidirectional encoder representation from transformers (BERT) model. We first use an advanced BERT language model to convert the input text into dynamic word vectors; then, we adopt a convolutional neural network (CNN) to obtain the relatively significant partial emotional characteristics of the text. After extraction, we use the bidirectional recurrent neural network (BiGRU) to bidirectionally capture the contextual feature message of the text. Finally, with the MultiHeadAttention mechanism we obtain correlations among the data in different information spaces from different subspaces so that the key information related to emotion in the text can be selectively extracted. The final emotional feature representation obtained is classified using Softmax. Compared with other similar existing methods, our model in this research paper showed a good effect in comparative experiments on an e-commerce text dataset, and the accuracy and F1-score of the classification were significantly improved.

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Section: Introductionmentioning

confidence: 99%

Chinese Multicategory Sentiment of E-Commerce Analysis Based on Deep Learning

Li,

Wang,

et al. 2023

Electronics

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“…Consequently, this inability to accurately assess emotional tone, such as sarcasm and humor, significantly limits the accuracy of the analysis. Multimodal Sentiment Analysis (MSA) combines these different modalities to provide a more comprehensive understanding of sentiment [4], which is important for market trend analysis, personalized marketing and brand management [5], which is important for market trend analysis, personalized marketing and brand management [6]- [8].In this study, we focus on addressing two major challenges in the field of multimodal sentiment analysis (MSA). First, the problem of inter-modality inconsistency, for instance, how to extract and integrate key features efficiently from different modalities (e.g.…”

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confidence: 99%

BCD-MM: Multimodal Sentiment Analysis Model With Dual-Bias-Aware Feature Learning and Attention Mechanisms

Ma,

Li,

Shao

et al. 2024

IEEE Access

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Multimodal Sentiment Analysis (MSA) is gaining attention. But faces two main challenges: effective feature extraction across modalities without redundancy, and removing spurious correlations between sentiment labels and multimodal features. In this paper, we propose a novel multimodal learning debiasing model, named Bilateral Cross-modal Debias Multimodal sentiment analysis Model (BCD-MM), to address these issues. To tackle the first challenge, our model uses an attention score-based method for critical information preservation and redundancy elimination within modalities, alongside a gated crossmodal attention mechanism for filtering inconsistencies through modal interaction. For the second challenge, BCD-MM features a debiasing approach with double bias extraction, utilizing a Tanh-based Mean Absolute Error (TMAE) loss function and inverse probability weighting to mitigate spurious correlations. The main objective of our model is to enhance the model's ability to generalize in out-of-distribution (OOD) situations by reducing the reliance on non-causal correlations. Extensive testing on three public datasets (MOSI, MOSEI, and SIMS) and two OOD datasets (OOD MOSI and OOD MOSEI) demonstrates our model's effectiveness in MSA and debiasing tasks.

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Knowledge-enhanced graph convolutional networks for Arabic aspect sentiment classification

Bensoltane,

Zaki

2023

Soc. Netw. Anal. Min.

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Sentiment Analysis in E-Commerce Platforms: A Review of Current Techniques and Future Directions

Cited by 18 publications

References 66 publications

Chinese Multicategory Sentiment of E-Commerce Analysis Based on Deep Learning

Chinese Multicategory Sentiment of E-Commerce Analysis Based on Deep Learning

BCD-MM: Multimodal Sentiment Analysis Model With Dual-Bias-Aware Feature Learning and Attention Mechanisms

Knowledge-enhanced graph convolutional networks for Arabic aspect sentiment classification

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