Sentiment-Aware Recommendation Systems in E-Commerce: A Review from a Natural Language Processing Perspective

Yogesh Gajula

REVIEW 3 major objections 5 minor 25 references

Sentiment-Aware Recommendation Systems in E-Commerce: A Review from a Natural Language Processing Perspective

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This review claims that adding sentiment analysis of review text to e-commerce recommenders improves accuracy, personalization, and explainability.

desk verdict A conventional survey that reads as a useful orientation to 2023–25 sentiment-aware recommenders, but its main evidence table has citation mismatches and the search methodology is too thin to support the 'comprehensive' label. read the letter →

arxiv 2505.03828 v1 pith:QEZFROBC submitted 2025-05-03 cs.IR cs.AIcs.CLcs.LG

Yogesh Gajula This is my paper · ORCID

classification cs.IRcs.AIcs.CLcs.LG

keywords sentiment-awarerecommendatione-commercenaturallanguageprocessingreview-basedrecommendertransformermodelsgraphneuralnetworksconversationalexplainablerecommendations

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Online stores mostly recommend by star ratings, leaving the opinions in review text unused. This review argues that e-commerce recommenders should mine those written reviews for sentiment and feed that signal into the recommendation pipeline. Covering 2023 to early 2025, it groups current work into four families: deep-learning encoders, transformer- and LLM-based models, graph neural networks that propagate sentiment, and conversational systems that adapt to feelings expressed in dialogue. If the reviewed evidence holds, review text is not a side channel but a load-bearing input that improves rating prediction, makes suggestions easier to explain, and helps cold-start situations where little interaction data exists.

What carries the argument

The central mechanism is the sentiment-extraction-to-scoring pipeline: raw reviews are passed through an NLP sentiment module that produces sentiment embeddings, sentiment scores, or aspect-level opinion vectors, and those signals are then merged with collaborative-filtering, graph, or dialogue state before ranking. This machinery does the work in every category of the review. In graph methods, sentiment becomes edge weights or relation types; in conversational systems, it becomes an entity-level emotional score that filters or re-weights candidates; in transformer methods, it is the encoded context that the scoring layer consumes. The implicit identity behind the whole review is that a user's opinion vector over item aspects is a better predictor of future preference than the scalar rating alone.

What would settle it

A systematic replication of the review's search, with full screening counts, that runs a sentiment-aware model against an otherwise identical rating-only baseline on the Amazon, Yelp, and TripAdvisor datasets; if review-text sentiment adds no consistent gain in RMSE, NDCG, or explanation quality across those datasets, the central claim fails.

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Extended reading notes

Core claim

The paper's central claim is that treating review text as a source of sentiment, not as a bag of words, lets recommender systems predict ratings and rankings more accurately while also producing recommendations that can be explained by the opinions that drove them. It organizes the 2023-2025 literature into four approach families: deep-learning classifiers that attach sentiment embeddings to user-item interactions; transformer-based methods that extract context-sensitive sentiment; graph neural networks that propagate sentiment signals through user-item-entity graphs; and conversational recommenders that re-weight or filter entities by the sentiment expressed in dialogue. The paper's demonstration is architectural: sentiment flows from raw reviews through an NLP extraction module into the scoring engine, and the resulting scores align with what users explicitly praised or criticized. It reports representative results such as BERT-based sentiment features improving precision and recall over ratings-only baselines, transformer encoders cutting RMSE and MAE relative to non-transformer deep networks, and a conversational system that avoids recommending items the user declared dislike for.

Load-bearing premise

The survey's conclusions rest on the five selected 2023-2025 papers being representative of the wider sentiment-aware recommendation literature.

Editorial extensions

If this is right

Adding review-text sentiment to rating-based collaborative filtering should lower rating-prediction error and improve ranking metrics such as precision, recall, and NDCG.
Recommendation explanations can cite which opinions drove the suggestion, for instance by noting that a user praised battery life, which increases transparency and user trust.
Conversational recommenders that filter entities by the user's expressed sentiment avoid recommending items the user explicitly disliked and can reach good recommendations in fewer dialogue turns.
Transformer- and LLM-based sentiment encoders reduce manual feature engineering and help cold-start cases where only text, such as item descriptions or early reviews, is available.
Because textual sentiment can carry and amplify societal biases, fairness-aware training and debiasing of sentiment analysis are necessary before these systems are widely deployed.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A direct but unstated corollary is that the four-way taxonomy can serve as a design menu: choose conversational sentiment monitoring when dialogue exists, graph propagation when entity relations are rich, and transformer encoders when review text is long and nuanced.
The review treats sentiment as a signal that can be plugged into existing recommender backbones; the harder claim that sentiment causes, rather than merely correlates with, better recommendations is left open, and the paper itself flags causal inference as future work.
Because only five models are summarized in detail, a focused replication that runs one sentiment-aware model against the same rating-only baseline across Amazon, Yelp, and TripAdvisor would test whether the claimed gains are robust to domain and dataset scale.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague—

You can skip this paper unless you want a quick tour of the 2023–25 sentiment-aware recommendation literature. It is a review, so there is no new model, dataset, or derivation. What it does well is organize recent work into four buckets—deep learning, transformers, graph networks, conversational systems—and give an accessible plain-language summary. The dataset overview (Amazon, Yelp, TripAdvisor, MovieLens) is handy for a newcomer, and the challenges section (noise, sarcasm, aspect alignment, cold start) names the real practical issues. If a practitioner asked me for a starting point, I'd point them here.

But the paper's load-bearing evidence is shakier than the prose. Table 1, the main summary of representative models, has citation mismatches that a reviewer would catch immediately. RAKCR is labeled [1], but [1] is Cambria et al.'s XAI/LLM survey; the actual RAKCR paper is [2]. Chat-Rec is labeled [20], but [20] is Gao et al.'s ChatGPT causal-reasoning paper, not the Chat-Rec work—and Section 2.2 cites [1] for Chat-Rec, which is also wrong. The 'Chen et al. (2023)' explanation work is attributed to [15], which is actually Said (2025). These are not cosmetic errors; they sit in the evidence table that supports the survey's central claim that sentiment improves accuracy and explainability. A reader cannot verify the 'notable findings' without guessing which paper is actually being described.

The search methodology is also underreported. One paragraph says four databases were searched, but no query strings, no screening counts, and no list of included studies are given. With only five models in Table 1, the claim of a comprehensive review of 2023–25 work is not established. And there is no synthesis of effect sizes or consistent baselines—the accuracy/explainability benefits are asserted from the original papers rather than compared across studies. The hypothesis may well be true; this manuscript just doesn't demonstrate it.

So: the paper has value as an orientation aid, but not as a reliable reference. The citation errors are fixable, but they are exactly the kind of thing that undermines a reader's trust in a review. I would send it to peer review with a request for major revision, mainly to correct the reference mapping and report a reproducible search process. I would not cite it in its current form.

Referee Report

3 major / 5 minor

Summary. This manuscript is a narrative literature review of sentiment-aware recommendation systems in e-commerce from an NLP perspective, covering work from 2023 to early 2025. It organizes the literature into four methodological categories—deep learning classifiers, transformer/LLM-based methods, graph neural networks, and conversational recommender systems—and summarizes representative models in Table 1 and datasets in Table 2. The abstract and introduction claim that integrating sentiment analysis into recommenders improves prediction accuracy and explainability through detailed opinion extraction. The review also discusses open challenges (noise, aspect alignment, dynamic preferences, cold start, scalability, evaluation, fairness) and proposes future research directions.

Significance. The topic is timely and practically important, and the paper provides a clearly structured entry point for NLP researchers working on e-commerce recommendation. Its strengths include the proposed four-category taxonomy, the concrete discussion of datasets and evaluation metrics, and the explicit enumeration of open challenges and future directions. However, the central claims about accuracy and explainability improvements rest on a small, partially mis-cited evidence set (Table 1) and a search methodology that is not reproducible. Once these verification issues are addressed, the review could serve as a useful synthesis; in its current form, its significance is limited by the lack of a defensible evidence base.

major comments (3)

[Table 1, Section 2] The citation labels in Table 1 do not consistently match the reference list, which undermines the verifiability of the paper's central evidence. For example, RAKCR is labeled [1], but reference [1] is Cambria et al.'s XAI/LLM survey; the actual RAKCR paper is reference [2]. Chat-Rec is labeled [20], but reference [20] is Gao et al.'s 'Is ChatGPT a good causal reasoner?' paper, not Chat-Rec; Section 2.2 also attributes Chat-Rec to [1]. Additionally, Section 2.3 attributes an explanation generation work to 'Chen et al. (2023)' via reference [15], which is Said (2025). Because Table 1 is the main support for the claimed trends and benefits, each row must trace to the correct reference or be removed. The authors should re-verify every citation in the manuscript, not only in Table 1.
[Section 2 (Literature Search Methodology)] The described systematic search is not reproducible and does not support the claim that the selected models are representative of 2023–2025 research. The methodology is reported in one paragraph with no full query strings, no screening counts (retrieved, deduplicated, screened, included), and no list of included studies. Without these elements, a reader cannot assess whether the five models in Table 1 are a selective convenience sample or a systematic synthesis. Please provide the exact search queries, the search date, PRISMA-style screening numbers, and either the full list of included studies or a link to a supplementary file.
[Section 3 (Results – Data & Metrics) and Abstract] The paper's central claim—that sentiment integration enhances accuracy and explainability—is asserted in the abstract and introduction but not synthesized from quantitative evidence in the review. Section 3 discusses metrics and reproducibility in general terms but reports no effect sizes, no confidence intervals, and no consistent rating-only baselines for the models in Table 1. The 'Notable Findings' column of Table 1 contains qualitative statements such as 'Outperforms vanilla GCN on rating prediction' and 'higher precision/recall than using ratings alone' without the underlying numbers or directions of improvement. To substantiate the claimed benefits, the authors should either add a summary table of reported metrics (e.g., RMSE, MAE, Precision@N, NDCG) for each cited model relative to its baseline, or explicitly frame the claims as 'reported in the cited studies' without implying that this review independently establishes them.

minor comments (5)

[Section 1 and Abstract] The abstract describes the review as 'comprehensive,' but the scope (2023–early 2025, five representative models) is narrow. Please qualify the scope language to avoid overstatement.
[Table 1] Table 1 contains a typo in the RAKCR row: 'improving personalization s' should be 'improving personalization.' Please proofread the table text.
[Section 3] The 'Standardized Evaluation and Reproducibility' paragraph lists many tools (MLflow, Weights & Biases, Docker, Hugging Face) in a way that reads as a general reproducibility checklist rather than a synthesis specific to sentiment-aware recommenders. Consider condensing this to recommendations that directly relate to the surveyed literature.
[Figure captions] Figures 1 and 2 are referenced in the text, but the figure images are not included in the manuscript text. Ensure that final figure files are provided and that the captions are self-contained.
[References] Several references lack author names or are incomplete, e.g., [11] 'Hotel-Review Datasets' and [12] 'Dataset list' have no author; [14] is a repository URL. For consistency, please complete all reference entries with authors where available.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey makes no derivation, fits no parameters, and its claims rest on external cited studies rather than on its own definitions or prior self-citations.

full rationale

This paper is a narrative literature review, not a derivation or an empirical study. It surveys external work from 2023 to early 2025, summarizes representative models in Table 1 and datasets in Table 2, and discusses challenges and future directions. There is no equation whose output is defined by its input, no fitted parameter that is later renamed as a prediction, and no central claim that is justified by a self-citation chain. The benefits of sentiment-aware recommendation asserted in the abstract and introduction are attributed to external papers (e.g., references [3], [4], [5], [8], [13]) rather than derived within the manuscript. The paper's own contributions are organizational and descriptive. The identified citation-number mismatches and the non-reproducible search description are serious correctness and evidence-quality concerns, but they are not circularity: a mis-cited external result is still an external result, not an input smuggled back as an output. Under the hard rules, a non-finding is appropriate, so the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

This is a review, so no free parameters or invented entities appear. The load is carried by domain assumptions about the value of sentiment text and the representativeness of the selected literature.

assumptions (3)

domain assumption User-generated review text contains sentiment information that numerical ratings do not capture.
Section 1 motivates the survey with the example 'The phone camera is great, but the battery life is disappointing,' asserting that sentiment text enriches recommendation signals.
domain assumption Incorporating review sentiment into recommender algorithms improves accuracy and explainability.
This is the central claim of the review, asserted in the abstract and Section 1 and repeated in Section 2. It is supported only by selected cited examples, not by a meta-analysis.
domain assumption The selected 2023-2025 corpus is representative of the field.
Section 2 describes a systematic search but provides no screening counts or list of all included papers, so the representativeness claim is assumed.

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Cite this review

Pith. "Pith review of Sentiment-Aware Recommendation Systems in E-Commerce: A Review from a Natural Language Processing Perspective." pith.science (2026). https://pith.science/paper/QEZFROBC

@misc{pith2026250503828,
  author       = {Pith},
  title        = {Pith review of: Sentiment-Aware Recommendation Systems in E-Commerce: A Review from a Natural Language Processing Perspective},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QEZFROBC}},
  note         = {Machine review of arXiv:2505.03828}
}

read the original abstract

E-commerce platforms generate vast volumes of user feedback, such as star ratings, written reviews, and comments. However, most recommendation engines rely primarily on numerical scores, often overlooking the nuanced opinions embedded in free text. This paper comprehensively reviews sentiment-aware recommendation systems from a natural language processing perspective, covering advancements from 2023 to early 2025. It highlights the benefits of integrating sentiment analysis into e-commerce recommenders to enhance prediction accuracy and explainability through detailed opinion extraction. Our survey categorizes recent work into four main approaches: deep learning classifiers that combine sentiment embeddings with user item interactions, transformer based methods for nuanced feature extraction, graph neural networks that propagate sentiment signals, and conversational recommenders that adapt in real time to user feedback. We summarize model architectures and demonstrate how sentiment flows through recommendation pipelines, impacting dialogue-based suggestions. Key challenges include handling noisy or sarcastic text, dynamic user preferences, and bias mitigation. Finally, we outline research gaps and provide a roadmap for developing smarter, fairer, and more user-centric recommendation tools.

Figures

Figures reproduced from arXiv: 2505.03828 by the authors.

**Figure 2.** Example of a conversational recommendation scenario (simplified from Liu et al. 2025). The left shows an attribute-based dialogue for a shopping query, and the right shows a generation-based movie recommendation dialogue. Notably, on the right, the user explicitly says “I didn’t like the movie” (negative sentiment about First Blood), but a sentiment-unaware system still recommends a similar movie (The Terminator), l… view at source ↗

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Reference graph

Works this paper leans on

25 extracted references · 17 canonical work pages

[1]

Cambria, E. et al. (2024) XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Models [Preprint]. doi:arxiv.org/abs/2407.15248

arXiv 2024
[2]

Cui, Y. et al. (2024) ‘RAKCR: Reviews sentiment-aware based knowledge graph convolutional networks for personalized recommendation’, Expert Systems with Applications, 248. doi:10.1016/j.eswa.2024.123403

work page arXiv 2024
[20]

The LLM performs sentiment analysis on user reviews and generates interactive recommendation explanations

2023 LLM-augmented Recommender – integrates a large language model (ChatGPT) into the recommendation loop. The LLM performs sentiment analysis on user reviews and generates interactive recommendation explanations. Improved cold-start recommendations by using GPT to infer user intent from minimal data Also produced more natural, explainable interactions, t...

work page 2023
[15]

(2025) ‘On explaining recommendations with large language models: A Review’, Frontiers in Big Data, 7

Said, A. (2025) ‘On explaining recommendations with large language models: A Review’, Frontiers in Big Data, 7. doi:10.3389/fdata.2024.1505284

arXiv 2025
[3]

Darraz, N. et al. (2025) ‘Integrated sentiment analysis with Bert for enhanced hybrid recommendation systems’, Expert Systems with Applications, 261. doi:10.1016/j.eswa.2024.125533

arXiv 2025
[4]

Gheewala, S. et al. (2024) ‘Exploiting deep transformer models in textual review based Recommender Systems’, Expert Systems with Applications, 235. doi:10.1016/j.eswa.2023.121120

work page arXiv 2024
[5]

and Yeom, S

Gheewala, S., Xu, S. and Yeom, S. (2025) ‘In-depth survey: Deep Learning in Recommender Systems—exploring prediction and ranking models, datasets, feature analysis, and emerging trends’, Neural Computing and Applications [Preprint]. doi:10.1007/s00521-024-10866-z

work page doi:10.1007/s00521-024-10866-z 2025
[6]

good”, “bad

2025 Multi-Layer Attention Encoder-Decoder Temporal CNN for sentiment analysis, combined with collaborative filtering. Uses an attentive CNN to classify review sentiment, addresses class imbalance with GAN -based oversampling, then injects sentiment polarity into a CF model for final recommendations. Outperformed state-of-the-art baselines on Amazon produ...

work page 2019

Show all 25 references

[7]

(2023) Large-Scale Amazon Reviews dataset, collected in 2023 by McAuley Lab, Amazon Reviews’23

Lab, M. (2023) Large-Scale Amazon Reviews dataset, collected in 2023 by McAuley Lab, Amazon Reviews’23. Available at: https://amazon-reviews-2023.github.io/

2023
[8]

Builds a movie knowledge graph (MAKG) and filters entities by sentiment scores (using a sentiment lexicon and prompt- based analysis) to inform recommendations

2025 Sentimentally Enhanced Conversational Recommender – a conversational RS that analyzes user’s emotional tone towards entities in dialogue. Builds a movie knowledge graph (MAKG) and filters entities by sentiment scores (using a sentiment lexicon and prompt- based analysis) ...

2025
[9]

Xiang, Y. et al. (2024) ‘Text understanding and generation using transformer models for intelligent e-commerce recommendations’, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024) [Preprint]. doi:10.1117/12.3034062

2024 doi
[10]

Krishna, E.S. et al. (2025) ‘Enhancing e-commerce recommendations with sentiment analysis using MLA-edtcnet and collaborative filtering’, Scientific Reports, 15(1). doi:10.1038/s41598-025-91275-7

2025 doi
[11]

Available at: https://www.cs.cmu.edu/~jiweil/html/hotel-review.html

(2024) Hotel-Review Datasets. Available at: https://www.cs.cmu.edu/~jiweil/html/hotel-review.html

2024
[12]

Liu, F. et al. (2025) ‘Sentimentally enhanced conversation recommender system’, Complex & Intelligent Systems, 11(2). doi:10.1007/s40747-024-01766-9

2025 doi
[13]

Hasan, E. et al. (2024) Review-based Recommender Systems: A survey of approaches, challenges and future perspectives, arXiv.org. Available at: https://arxiv.org/abs/2405.05562

2024 arXiv
[14]

Xu, X. et al. (2025) Enhancing user intent for recommendation systems via large language models, pp. 1–2. doi:10.20944/preprints202501.0627.v1

2025
[16]

Available at: https://recbole.io/dataset_list.html

Dataset list (2024) RecBole. Available at: https://recbole.io/dataset_list.html

2024
[17]

and Zhou, D

Zhang, X., Zhang, L. and Zhou, D. (2023) ‘Sentiment analysis on streaming user reviews via dual-channel Dynamic Graph Neural Network’, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing [Preprint].doi:10.18653/v1/2023.emnlp-main.446

2023 doi
[18]

One of the first GNN -based recommenders to include sentiment

2022 Sentiment-Enhanced Neural Graph Recommender – employs a graph neural network (GNN) to propagate both user-item interactions and sentiment signals (from review text). One of the first GNN -based recommenders to include sentiment. Reported better performance on social recom...

2022
[19]

Available at: https://paperswithcode.com/dataset/yelp (Accessed: May 2024)

Papers with code - yelp dataset (2024) Dataset | Papers With Code. Available at: https://paperswithcode.com/dataset/yelp (Accessed: May 2024)

2024
[21]

Sharma, K. et al. (2024) ‘A survey of graph neural networks for social recommender systems’, ACM Computing Surveys, 56(10), pp. 1–34. doi:10.1145/3661821

2024 doi
[22]

Shi, L. et al. (2022) ‘SENGR: Sentiment-enhanced neural graph recommender’, Information Sciences, 589, pp. 655–

2022
[24]

and Zukerman, I

Seroussi, Y., Bohnert, F. and Zukerman, I. (2011) ‘Personalised rating prediction for new users using Latent Factor Models’, Proceedings of the 22nd ACM conference on Hypertext and hypermedia [Preprint]. doi:10.1145/1995966.1995976

2011
[25]

Gao, J. et al. (2023) ‘Is chatgpt a good causal reasoner? A comprehensive evaluation’, Findings of the Association for Computational Linguistics: EMNLP 2023 [Preprint]. doi:10.18653/v1/2023.findings-emnlp.743

2023 doi
[669]

doi:10.1016/j.ins.2021.12.120

2021 doi

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

Cambria, E. et al. (2024) XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Models [Preprint]. doi:arxiv.org/abs/2407.15248

arXiv 2024

[2] [2]

Cui, Y. et al. (2024) ‘RAKCR: Reviews sentiment-aware based knowledge graph convolutional networks for personalized recommendation’, Expert Systems with Applications, 248. doi:10.1016/j.eswa.2024.123403

work page arXiv 2024

[3] [20]

The LLM performs sentiment analysis on user reviews and generates interactive recommendation explanations

2023 LLM-augmented Recommender – integrates a large language model (ChatGPT) into the recommendation loop. The LLM performs sentiment analysis on user reviews and generates interactive recommendation explanations. Improved cold-start recommendations by using GPT to infer user intent from minimal data Also produced more natural, explainable interactions, t...

work page 2023

[4] [15]

(2025) ‘On explaining recommendations with large language models: A Review’, Frontiers in Big Data, 7

Said, A. (2025) ‘On explaining recommendations with large language models: A Review’, Frontiers in Big Data, 7. doi:10.3389/fdata.2024.1505284

arXiv 2025

[5] [3]

Darraz, N. et al. (2025) ‘Integrated sentiment analysis with Bert for enhanced hybrid recommendation systems’, Expert Systems with Applications, 261. doi:10.1016/j.eswa.2024.125533

arXiv 2025

[6] [4]

Gheewala, S. et al. (2024) ‘Exploiting deep transformer models in textual review based Recommender Systems’, Expert Systems with Applications, 235. doi:10.1016/j.eswa.2023.121120

work page arXiv 2024

[7] [5]

and Yeom, S

Gheewala, S., Xu, S. and Yeom, S. (2025) ‘In-depth survey: Deep Learning in Recommender Systems—exploring prediction and ranking models, datasets, feature analysis, and emerging trends’, Neural Computing and Applications [Preprint]. doi:10.1007/s00521-024-10866-z

work page doi:10.1007/s00521-024-10866-z 2025

[8] [6]

good”, “bad

2025 Multi-Layer Attention Encoder-Decoder Temporal CNN for sentiment analysis, combined with collaborative filtering. Uses an attentive CNN to classify review sentiment, addresses class imbalance with GAN -based oversampling, then injects sentiment polarity into a CF model for final recommendations. Outperformed state-of-the-art baselines on Amazon produ...

work page 2019