A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis

Fandong Meng; Jie Zhou; Jinan Xu; Jinchao Zhang; Yufeng Chen; Yunlong Liang

REVIEW 4 major objections 4 minor 63 references

A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis

T0 review · 4 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Aspect-guided encoding tops prior sentiment models without extra features.

desk verdict A real but incremental extension of the DTMT line; the claimed SOTA margins rest on quoted baselines and a test-set-chosen depth, so treat the numbers as provisional. read the letter →

arxiv 1909.00324 v1 pith:VUVF2QIM submitted 2019-09-01 cs.CL

Yunlong Liang , Fandong Meng , Jinchao Zhang , Jinan Xu , Yufeng Chen , Jie Zhou This is my paper · ORCID

classification cs.CL

keywords aspect-basedsentimentanalysisaspect-guidedencoderdeeptransitionarchitectureaspectreconstructiongatedrecurrentunitclassificationSemEval2014aspect-specificrepresentation

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

This paper argues that aspect-based sentiment analysis fails when the sentence encoder is built without reference to the aspect under consideration, because background words get mixed into the representation. It proposes AGDT, an encoder that conditions every step of sentence encoding on the given aspect through an aspect-gated recurrent unit, and adds an auxiliary objective that forces the sentence representation to be able to reconstruct the aspect. On four SemEval datasets covering both aspect-category and aspect-term subtasks, AGDT reports higher accuracy than the strongest previous models trained without extra features, with the largest gains on sentences that carry different sentiments for different aspects. The result matters because it suggests a concrete architectural principle: the target of prediction should shape representation building from the first layer, not only the final attention or gating stage.

What carries the argument

The load-bearing mechanism is the aspect-guided encoder built from a new A-GRU unit followed by T-GRU transition layers. Inside the A-GRU, an aspect-gate $g_t = \mathrm{relu}(W_a a + W_{hg} h_{t-1})$—computed from the fixed aspect embedding $a$ and the previous hidden state—controls how strongly each input token $x_t$ enters the candidate activation through both nonlinear and linear paths, so aspect-irrelevant words are down-weighted while encoding happens. The T-GRUs are zero-input GRU transitions that deepen the recurrent computation at each time step. A second component, aspect reconstruction, adds an auxiliary loss that asks the pooled sentence representation to predict the aspect (softmax cross-entropy for categories, sigmoid cross-entropy for term words), and the final objective $J = -\sum_i y_i \log p_i + \lambda L$ combines sentiment classification with this reconstruction term. The design claim is that guidance from the very first encoding step plus reconstruction pressure produces representations that already contain the relevant aspect-specific features before any downstream attention.

What would settle it

Take the released code for GCAE, ATAE-LSTM, TD-LSTM, RAM, and IAN and retrain them under AGDT's exact preprocessing, tokenization, embedding initialization, optimizer settings, and repeated five-run protocol; if their accuracies rise to AGDT's level or the reported margins shrink to insignificance, the claim of same-setting state-of-the-art performance is not supported.

Watch

Extended reading notes

Core claim

AGDT claims that an aspect-specific sentence representation—built by letting the aspect modulate both the nonlinear and linear transformations of each input word embedding at every time step, then passed through stacked transition layers and pooled—yields more accurate sentiment polarity predictions than encoders that first build a general sentence representation and only later attend to or gate by the aspect. The paper reports accuracy increases over GCAE of +2.43 and +1.63 points on the aspect-category datasets and +1.57 and +2.36 points on the aspect-term datasets. On a hard subset in which one sentence has different sentiment labels for different aspects, the improvements are much larger, up to +11.47 points on Restaurant-14 HDS. The authors also show that removing the aspect-gate, the reconstruction objective, or the deep transition layers each lowers accuracy, and that the model can reconstruct the given aspect from its sentence representation with high accuracy. They conclude that AGDT achieves state-of-the-art performance among models that do not use additional features such as BERT.

Load-bearing premise

The central comparison assumes that the baseline accuracy scores quoted from earlier papers were obtained under the same data preprocessing, hyperparameter choices, and training settings as AGDT, so the reported margins reflect a head-to-head difference rather than differences in setup.

Editorial extensions

If this is right

On the four datasets tested, AGDT surpasses the same-setting baselines, with reported gains over GCAE of +2.43/+1.63 on aspect-category data and +1.57/+2.36 on aspect-term data.
The hard-subset results imply the architecture is especially useful when a single sentence expresses different polarities for different aspects, where AGDT gains up to +11.47 accuracy points over GCAE.
Ablations imply each of the three design choices—deep transitions, the aspect-gate, and the reconstruction objective—contributes, and that the aspect-gate is the largest single factor on the hard subsets.
Without resorting to BERT or external knowledge, AGDT reports state-of-the-art accuracy on the standard SemEval 2014 setups, with the only noted exception being a feature-augmented model on the laptop three-class setting.
The aspect-reconstruction accuracies (about 99% on category sets and 71–76% on term sets) indicate the sentence representation indeed retains aspect-specific information, supporting the paper's explanation for the gains.

Reading between the lines

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

If the aspect-gate principle generalizes, target-conditioned encoders should also help other tasks where a conditioning entity must disambiguate context, such as relation extraction with specified entity mentions or targeted hate-speech detection against a named target.
The large hard-subset gains suggest that real-world deployments should measure accuracy on multi-aspect sentences separately, since that is where an aspect-guided encoder earns its added complexity.
The reconstruction objective is a cheap auxiliary loss that could be applied to low-resource domains or weakly labeled data as a form of self-supervision, because it needs only the aspect labels already present in the training set.
Combining AGDT with pretrained language model features—left as future work by the authors—is a natural next step that would likely compound the gains, since BERT features are largely orthogonal to the aspect-gating mechanism.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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

Quick take: this is a solid, well-scoped empirical paper for pre-BERT ABSA. The genuinely new piece is the A-GRU, an aspect-gated GRU at the bottom of a deep transition stack, together with an auxiliary aspect-reconstruction loss. That combination is not in the cited L-GRU/DTMT work or in GCAE. The ablations are useful: each component (deep transition, aspect gate, reconstruction, aspect concatenation) contributes, and the HDS split is a sensible stress test for sentences with conflicting polarities. The code is public, which is a real plus.

The reader's main worry is fair. Every baseline in Tables 4, 5, and 10 is quoted from the original papers, not re-run under AGDT's training protocol. The 'same setting' claim is therefore an assumption, not a measurement. Hyperparameters, preprocessing, and conflict-label handling can easily shift a point or two, and the claimed margins are in that range. The stress-test note's proposed head-to-head rerun is the right experiment. The second issue is the transition depth: Table 8 selects depth based on test-set accuracy. That is genuine test-set tuning and adds optimism. The effect is modest around the chosen depth, but it still means the reported numbers are not a clean evaluation. There is also a wording problem: the abstract says 'significantly outperforms ... with the same setting' while Table 10 has AGDT below TNet on Laptop. The 'additional features' explanation is plausible, but the abstract overstates. And no significance tests are reported, so 'significantly' is doing too much work.

The math is straightforward and there is no circularity; the reconstruction loss is an auxiliary training objective, not a fitted prediction. The citation pattern is honest and clearly positions the work as an extension of the authors' own DTMT line, which is fine because that prior result is published and publicly checkable. The core weakness is evaluation control, not the model itself.

I would send this to a serious peer reviewer rather than desk reject. The contribution is incremental but real, and the public code and four datasets make the requested rerun feasible. A referee can reasonably ask for a controlled baseline reimplementation and a depth chosen on validation; if those hold, the paper is a decent non-BERT ABSA result. For anyone working outside the BERT-dominated part of ABSA, this is worth knowing about. I would not cite it in the next twelve months unless I was specifically comparing pre-BERT models.

Referee Report

4 major / 4 minor

Summary. The paper introduces AGDT (Aspect-Guided Deep Transition), a neural model for aspect-based sentiment analysis (ABSA). The model consists of an aspect-guided GRU (A-GRU) that incorporates the aspect embedding through a gate at every time step, followed by a stack of transition GRUs (T-GRUs) that deepen the representation; an auxiliary aspect-reconstruction loss is added to the sentiment classification loss, and the final representation is formed by concatenating the aspect embedding with the max-pooled sentence representation. The authors evaluate on four SemEval datasets across two subtasks (aspect-category and aspect-term), including "hard" subsets (HDS) containing sentences with conflicting polarities for different aspects, and also report a three-class comparison. They claim that AGDT significantly outperforms existing models under the same setting and achieves state-of-the-art performance among models that do not use additional features such as BERT.

Significance. The proposed architecture is a reasonable and internally coherent extension of deep transition networks to ABSA, and the ablation study indicates that each component (aspect-guided encoding, aspect reconstruction, aspect concatenation) contributes positively, particularly on the HDS subsets. The public code, five repeated runs with reported standard deviations, and the reconstruction-accuracy analysis are strengths. If the comparison with prior work were controlled, the reported gains on the HDS subsets would be a meaningful contribution to the literature. However, the central empirical claim of state-of-the-art performance rests on baseline numbers quoted from prior papers and on hyperparameters (transition depth, reconstruction-loss weight) selected using test-set results, which substantially weaken the confidence in the headline margins.

major comments (4)

[§3.3 and Tables 4, 5, 10] The claim in the abstract and §1 that AGDT "significantly outperforms existing models with the same setting" is not supported by controlled experiments: all baseline results in Tables 4, 5, and 10 are marked '*' and quoted from prior publications (GCAE or the original papers), not re-run under the protocol of §3.2 (fixed 300d GloVe, Adam lr 0.01, batch size 4096 at token level, dropout 0.5/0.3, gradient clipping 5). Since preprocessing details such as treatment of the "conflict" label, tokenization, and hyperparameter tuning may differ across the original papers, the reported margins (e.g., +2.43 and +1.63 on aspect-category DS, +1.57 and +2.36 on aspect-term DS over GCAE) could shrink or disappear in a head-to-head comparison. The authors should re-run at least GCAE and preferably the other baselines under their exact setup, or explicitly reframe the claim as "improvement over published numbers" rather than "same setting."
[§3.4, Table 8] The transition depth is selected on the test sets: Table 8 reports test-set accuracy for depths 1 through 6, and the text states that depth 4 is chosen because it is best "at most case." This is a form of test-set peeking that adds optimism to all reported AGDT numbers. Moreover, the choice is not uniformly supported by the table: on D2 (Restaurant-Large) depth 3 and depth 4 differ by only 0.02 on DS and depth 4 is not the best on HDS; on D4 (Laptop) depth 3 has higher DS accuracy than depth 4 (71.62 vs. 71.50). The depth should be selected on a development split (or via cross-validation), with test results reported only after fixing the depth, and the sensitivity of the final numbers to this choice should be discussed.
[Abstract, §3.4, Tables 4 and 5] The word "significantly" is used without statistical testing. Although the paper reports means and standard deviations over five runs for AGDT and GCAE, there is no paired significance test (e.g., bootstrap or approximate randomization) comparing AGDT against the baselines, and the baselines are not re-run in the same environment. For Table 10, no standard deviations are given at all. The authors should either perform significance tests on re-run baselines or replace "significantly" with language such as "consistently higher" to avoid an unsupported statistical claim.
[§3.4, Table 10 and related text] The claim of state-of-the-art among models without additional features is defined post hoc: TNet outperforms AGDT on Laptop (76.54 vs. 75.86), and the paper explains that TNet uses additional features (position features, local n-grams, word-level features), but this is not marked in Table 10. If the comparison is meant to exclude models with such features, TNet should either be flagged in the table or the exclusion criterion should be specified in advance. Otherwise, the statement that AGDT achieves the best result "among the models without using additional features" is a judgment that depends on a classification of the baseline that is not systematically applied to all entries.

minor comments (4)

[Figures 3 and 4] In the manuscript version I received, Figures 3 and 4 contain garbled Unicode escape sequences (e.g., "/uni00000057/uni00000048...") instead of readable text or heat-map labels. These figures need to be regenerated with proper glyphs, because the case-study discussion refers directly to the visualization of attention weights.
[§3.4, Table 8 and text] The phrase "at most case" (second paragraph of §3.4) is awkward and should be reworded; I believe the authors mean "in most cases" or "for most datasets."
[§2.2, Eq. (13)-(15)] The notation for the aspect-reconstruction loss is a bit confusing: C1 and C2 are described as the number of predefined aspects and the number of distinct words in all terms, respectively, but the sums start at i=0 and there is no definition of the range of i. Also, in Eq. (15) the "underlined part" is referred to in the text but the underline is not visible in the equation; this should be fixed.
[§3.2 and §3.4, lambda selection] The reconstruction-loss weight lambda is said to be "fine-tuned" in §3.2 and then described in §3.4 as selected on a "temporary development set" sampled from the HDS part of the training set. This is not a standard dev/test split, and the details of the sampling (size, randomness, how many trials) are missing. Please describe the selection procedure precisely, including how the reported test numbers are affected by the choice.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: AGDT's claims are empirical benchmark comparisons; quoted baselines and test-set tuning are validity risks, not circular reductions.

full rationale

The paper makes no first-principles derivation claim; AGDT is an empirical model whose support is test-set accuracy on SemEval datasets. The aspect-reconstruction term (Eqs. 13-14) is an auxiliary training objective inside the final loss (Eq. 15), not a fitted quantity later relabeled as a prediction, so no circular loop is created. Baseines in Tables 4, 5, and 10 are quoted from prior papers, which is a comparability and reproducibility threat but not a constructional equivalence. Selecting transition depth from test-set behavior (Table 8) is selection bias rather than a definitional reduction. Self-citations to Meng and Zhang (2019) for the deep-transition and L-GRU building blocks are architectural provenance, and the accuracy claims are not justified by that citation. The central claim is an external benchmark comparison, so no step reduces by construction to its own inputs.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central claim is empirical, so the ledger contains training hyperparameters rather than physical quantities. The two listed free parameters (lambda and depth) affect the reported scores; depth was chosen using the test set, which is a mild methodological risk. No new entities are postulated.

free parameters (2)

lambda (reconstruction loss weight) = 0.4 (Restaurant-14), 0.4 (Restaurant-Large), 0.2 (Restaurant), 0.5 (Laptop)
Tuned on a temporary development set sampled from the HDS part of the training set (Section 3.4, Figure 2).
transition depth = 4
Selected after varying depth 1-6 and comparing test-set accuracy (Table 8), a test-set-based choice.

assumptions (3)

domain assumption Pre-trained 300d GloVe embeddings provide semantic representations adequate for ABSA.
Word embeddings are initialized with GloVe and fixed (Section 3.2).
domain assumption The SemEval 2014 datasets and accuracy metric are valid instruments for measuring ABSA performance.
Evaluation follows standard practice of the field (Section 3.1).
domain assumption The hard dataset (HDS) with conflicting sentiments across aspects is a meaningful diagnostic subset.
HDS is extracted from each DS to test aspect sensitivity (Section 3.1).

how reviews work

0 comments

Cite this review

Pith. "Pith review of A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis." pith.science (2026). https://pith.science/paper/VUVF2QIM

@misc{pith2026190900324,
  author       = {Pith},
  title        = {Pith review of: A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VUVF2QIM}},
  note         = {Machine review of arXiv:1909.00324}
}

read the original abstract

Aspect based sentiment analysis (ABSA) aims to identify the sentiment polarity towards the given aspect in a sentence, while previous models typically exploit an aspect-independent (weakly associative) encoder for sentence representation generation. In this paper, we propose a novel Aspect-Guided Deep Transition model, named AGDT, which utilizes the given aspect to guide the sentence encoding from scratch with the specially-designed deep transition architecture. Furthermore, an aspect-oriented objective is designed to enforce AGDT to reconstruct the given aspect with the generated sentence representation. In doing so, our AGDT can accurately generate aspect-specific sentence representation, and thus conduct more accurate sentiment predictions. Experimental results on multiple SemEval datasets demonstrate the effectiveness of our proposed approach, which significantly outperforms the best reported results with the same setting.

Figures

Figures reproduced from arXiv: 1909.00324 by the authors.

**Figure 2.** The impact of λ w.r.t. accuracy on “HDS”. AC AG AR Rest-14 Rest-Large DS HDS DS HDS GRU √ × × 80.90 53.93 86.75 68.46 1 DT √ × × 81.74 56.63 87.54 72.39 2 √ √ × 81.88 60.42 87.72 74.81 3 × √ × 81.95 59.33 87.68 74.44 4 × √ √ 81.83 61.35 87.34 75.56 5 √ √ √ 81.78 62.02 87.55 75.73 6 [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** The output of A-GRU. overpricedjapanese food with mediocre service service service 0.0 0.5 1.0 [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: The above is the output of A-GRU. The bot [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

63 extracted references · 38 canonical work pages

[1]

URL: " 'urlintro :=

ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
[3]

Md Shad Akhtar, Dushyant Chauhan, Deepanway Ghosal, Soujanya Poria, Asif Ekbal, and Pushpak Bhattacharyya. 2019. https://doi.org/10.18653/v1/N19-1034 Multi-task learning for multi-modal emotion recognition and sentiment analysis . In Proceedings of the 2019 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human L...

work page doi:10.18653/v1/n19-1034 2019
[4]

Stefanos Angelidis and Mirella Lapata. 2018. http://arxiv.org/abs/1808.08858 Summarizing opinions: Aspect extraction meets sentiment prediction and they are both weakly supervised . CoRR, abs/1808.08858

arXiv 2018
[5]

Lingxian Bao, Patrik Lambert, and Toni Badia. 2019. https://www.aclweb.org/anthology/P19-2035 Attention and lexicon regularized LSTM for aspect-based sentiment analysis . In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 253--259, Florence, Italy. Association for Computational Linguistics

work page 2019
[6]

Chen , R

F. Chen , R. Ji , J. Su , D. Cao , and Y. Gao . 2018. https://doi.org/10.1109/TMM.2017.2757769 Predicting microblog sentiments via weakly supervised multimodal deep learning . IEEE Transactions on Multimedia, 20(4):997--1007

work page arXiv 2018
[7]

Peng Chen, Zhongqian Sun, Lidong Bing, and Wei Yang. 2017. https://doi.org/10.18653/v1/D17-1047 Recurrent attention network on memory for aspect sentiment analysis . In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 452--461. Association for Computational Linguistics

doi:10.18653/v1/d17-1047 2017
[8]

Zhuang Chen and Tieyun Qian. 2019. https://www.aclweb.org/anthology/P19-1052 Transfer capsule network for aspect level sentiment classification . In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 547--556, Florence, Italy. Association for Computational Linguistics

work page 2019

Show all 63 references

[9]

Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. https://doi.org/10.3115/v1/D14-1179 Learning phrase representations using rnn encoder--decoder for statistical machine translation . In Proceedings ...

2014 doi
[10]

Hongliang Dai and Yangqiu Song. 2019. https://www.aclweb.org/anthology/P19-1520 Neural aspect and opinion term extraction with mined rules as weak supervision . In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5268--5277, Floren...

2019
[11]

Jacob Devlin, Ming - Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. http://arxiv.org/abs/1810.04805 BERT: pre-training of deep bidirectional transformers for language understanding . CoRR, abs/1810.04805

2018 arXiv
[12]

Junwen Duan, Xiao Ding, and Ting Liu. 2018. https://doi.org/10.18653/v1/N18-1051 Learning sentence representations over tree structures for target-dependent classification . In Proceedings of the 2018 Conference of the North A merican Chapter of the Association for Computation...

2018 doi
[13]

Feifan Fan, Yansong Feng, and Dongyan Zhao. 2018. http://aclweb.org/anthology/D18-1380 Multi-grained attention network for aspect-level sentiment classification . In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3433--3442. Assoc...

2018
[14]

Shuqin Gu, Lipeng Zhang, Yuexian Hou, and Yin Song. 2018. http://aclweb.org/anthology/C18-1066 A position-aware bidirectional attention network for aspect-level sentiment analysis . In Proceedings of the 27th International Conference on Computational Linguistics, pages 774--78...

2018
[15]

Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2018 a . https://www.aclweb.org/anthology/C18-1096 Effective attention modeling for aspect-level sentiment classification . In Proceedings of the 27th International Conference on Computational Linguistics, pages 1121--...

2018
[16]

Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2018 b . https://doi.org/10.18653/v1/P18-2092 Exploiting document knowledge for aspect-level sentiment classification . In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume...

2018 doi
[17]

Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2019. https://www.aclweb.org/anthology/P19-1048 An interactive multi-task learning network for end-to-end aspect-based sentiment analysis . In Proceedings of the 57th Annual Meeting of the Association for Computational...

2019
[18]

Sepp Hochreiter and J\" u rgen Schmidhuber. 1997. https://doi.org/10.1162/neco.1997.9.8.1735 Long short-term memory . Neural Comput., 9(8):1735--1780

1997 doi
[19]

Minghao Hu, Yuxing Peng, Zhen Huang, Dongsheng Li, and Yiwei Lv. 2019. https://www.aclweb.org/anthology/P19-1051 Open-domain targeted sentiment analysis via span-based extraction and classification . In Proceedings of the 57th Annual Meeting of the Association for Computationa...

2019
[20]

Binxuan Huang and Kathleen Carley. 2018. http://aclweb.org/anthology/D18-1136 Parameterized convolutional neural networks for aspect level sentiment classification . In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1091--1096. As...

2018
[21]

Binxuan Huang, Yanglan Ou, and Kathleen M. Carley. 2018. http://arxiv.org/abs/1804.06536 Aspect level sentiment classification with attention-over-attention neural networks . CoRR, abs/1804.06536

2018 arXiv
[22]

Long Jiang, Mo Yu, Ming Zhou, Xiaohua Liu, and Tiejun Zhao. 2011. https://www.aclweb.org/anthology/P11-1016 Target-dependent twitter sentiment classification . In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologi...

2011
[23]

Yoon Kim. 2014. http://arxiv.org/abs/1408.5882 Convolutional neural networks for sentence classification . CoRR, abs/1408.5882

2014 arXiv
[24]

Kingma and Jimmy Ba

Diederik P. Kingma and Jimmy Ba. 2014. http://arxiv.org/abs/1412.6980 Adam: A method for stochastic optimization . CoRR, abs/1412.6980

2014 arXiv
[25]

Xin Li, Lidong Bing, Wai Lam, and Bei Shi. 2018 a . http://aclweb.org/anthology/P18-1087 Transformation networks for target-oriented sentiment classification . In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), p...

2018
[26]

Xin Li, Lidong Bing, Piji Li, and Wai Lam. 2018 b . http://arxiv.org/abs/1811.05082 A unified model for opinion target extraction and target sentiment prediction . CoRR, abs/1811.05082

2018 arXiv
[27]

Bin Liang, Jiachen Du, Ruifeng Xu, Binyang Li, and Hejiao Huang. 2019. https://www.aclweb.org/anthology/P19-1462 Context-aware embedding for targeted aspect-based sentiment analysis . In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, p...

2019
[28]

Jiangming Liu and Yue Zhang. 2017. http://aclweb.org/anthology/E17-2091 Attention modeling for targeted sentiment . In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 572--577. Associati...

2017
[29]

Huaishao Luo, Tianrui Li, Bing Liu, and Junbo Zhang. 2019. https://www.aclweb.org/anthology/P19-1056 DOER : Dual cross-shared RNN for aspect term-polarity co-extraction . In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 591--601...

2019
[30]

Thang Luong, Hieu Pham, and Christopher D. Manning. 2015. https://doi.org/10.18653/v1/D15-1166 Effective approaches to attention-based neural machine translation . In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1412--1421. Asso...

2015 doi
[31]

Dehong Ma, Sujian Li, and Houfeng Wang. 2018. https://www.aclweb.org/anthology/D18-1504 Joint learning for targeted sentiment analysis . In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4737--4742, Brussels, Belgium. Association ...

2018
[32]

Dehong Ma, Sujian Li, Xiaodong Zhang, and Houfeng Wang. 2017. http://dl.acm.org/citation.cfm?id=3171837.3171854 Interactive attention networks for aspect-level sentiment classification . In Proceedings of the 26th International Joint Conference on Artificial Intelligence, IJCA...

2017
[33]

Navonil Majumder, Soujanya Poria, Alexander Gelbukh, Md Shad Akhtar, Erik Cambria, and Asif Ekbal. 2018. http://aclweb.org/anthology/D18-1377 Iarm: Inter-aspect relation modeling with memory networks in aspect-based sentiment analysis . In Proceedings of the 2018 Conference on...

2018
[34]

Fandong Meng and Jinchao Zhang. 2019. http://arxiv.org/abs/1812.07807 DTMT: A novel deep transition architecture for neural machine translation . CoRR, abs/1812.07807

2019 arXiv
[35]

Antonio Valerio Miceli Barone, Jind r ich Helcl, Rico Sennrich, Barry Haddow, and Alexandra Birch. 2017. https://doi.org/10.18653/v1/W17-4710 Deep architectures for neural machine translation . In Proceedings of the Second Conference on Machine Translation, pages 99--107. Asso...

2017 doi
[36]

Razvan Pascanu, Çaglar Gülçehre, Kyunghyun Cho, and Yoshua Bengio. 2014. http://dblp.uni-trier.de/db/journals/corr/corr1312.html#PascanuGCB13 How to construct deep recurrent neural networks. CoRR, abs/1312.6026

2014 arXiv
[37]

Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2012. http://arxiv.org/abs/1211.5063 Understanding the exploding gradient problem . CoRR, abs/1211.5063

2012 arXiv
[38]

Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. https://doi.org/10.3115/v1/D14-1162 Glove: Global vectors for word representation . In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532--1543. Associati...

2014 doi
[39]

Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. 2014. https://doi.org/10.3115/v1/S14-2004 Semeval-2014 task 4: Aspect based sentiment analysis . In Proceedings of the 8th International Workshop on Semantic Eval...

2014 doi
[40]

Sebastian Ruder, Parsa Ghaffari, and John G. Breslin. 2016 a . https://doi.org/10.18653/v1/D16-1103 A hierarchical model of reviews for aspect-based sentiment analysis . In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 999--1005,...

2016 doi
[41]

Sebastian Ruder, Parsa Ghaffari, and John G. Breslin. 2016 b . https://doi.org/10.18653/v1/S16-1053 Insight-1 at semeval-2016 task 5: Deep learning for multilingual aspect-based sentiment analysis . In Proceedings of the 10th International Workshop on Semantic Evaluation (SemE...

2016 doi
[42]

Martin Schmitt, Simon Steinheber, Konrad Schreiber, and Benjamin Roth. 2018. https://www.aclweb.org/anthology/D18-1139 Joint aspect and polarity classification for aspect-based sentiment analysis with end-to-end neural networks . In Proceedings of the 2018 Conference on Empiri...

2018
[43]

Lei Shu, Hu Xu, and Bing Liu. 2017. http://arxiv.org/abs/1705.00251 Lifelong learning CRF for supervised aspect extraction . CoRR, abs/1705.00251

2017 arXiv
[44]

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. http://dl.acm.org/citation.cfm?id=2627435.2670313 Dropout: A simple way to prevent neural networks from overfitting . J. Mach. Learn. Res., 15(1):1929--1958

2014
[45]

Chi Sun, Luyao Huang, and Xipeng Qiu. 2019. http://arxiv.org/abs/1903.09588 Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence . CoRR, abs/1903.09588

2019 arXiv
[46]

Duyu Tang, Bing Qin, Xiaocheng Feng, and Ting Liu. 2016 a . http://aclweb.org/anthology/C16-1311 Effective lstms for target-dependent sentiment classification . In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pag...

2016
[47]

Duyu Tang, Bing Qin, and Ting Liu. 2016 b . https://doi.org/10.18653/v1/D16-1021 Aspect level sentiment classification with deep memory network . In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 214--224. Association for Computat...

2016 doi
[48]

Jialong Tang, Ziyao Lu, Jinsong Su, Yubin Ge, Linfeng Song, Le Sun, and Jiebo Luo. 2019. https://www.aclweb.org/anthology/P19-1053 Progressive self-supervised attention learning for aspect-level sentiment analysis . In Proceedings of the 57th Annual Meeting of the Association ...

2019
[49]

Yi Tay, Anh Tuan Luu, and Siu Cheung Hui. 2017. http://arxiv.org/abs/1712.05403 Learning to attend via word-aspect associative fusion for aspect-based sentiment analysis . CoRR, abs/1712.05403

2017 arXiv
[50]

Tan Thongtan and Tanasanee Phienthrakul. 2019. https://www.aclweb.org/anthology/P19-2057 Sentiment classification using document embeddings trained with cosine similarity . In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Rese...

2019
[51]

Jingjing Wang, Changlong Sun, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, and Guodong Zhou. 2019 a . https://www.aclweb.org/anthology/P19-1345 Aspect sentiment classification towards question-answering with reinforced bidirectional attention network . In Proceedings of the ...

2019
[52]

Shuai Wang, Sahisnu Mazumder, Bing Liu, Mianwei Zhou, and Yi Chang. 2018. http://aclweb.org/anthology/P18-1088 Target-sensitive memory networks for aspect sentiment classification . In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Vol...

2018
[53]

Yequan Wang, Minlie Huang, xiaoyan zhu, and Li Zhao. 2016. https://doi.org/10.18653/v1/D16-1058 Attention-based lstm for aspect-level sentiment classification . In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 606--615. Associati...

2016 doi
[54]

Yequan Wang, Aixin Sun, Minlie Huang, and Xiaoyan Zhu. 2019 b . https://doi.org/10.1145/3308558.3313750 Aspect-level sentiment analysis using as-capsules . In The World Wide Web Conference, WWW '19, pages 2033--2044, New York, NY, USA. ACM

2019
[55]

Jason Weston, Sumit Chopra, and Antoine Bordes. 2014. http://arxiv.org/abs/1410.3916 Memory networks . CoRR, abs/1410.3916

2014 arXiv
[56]

Bowen Xing, Lejian Liao, Dandan Song, Jingang Wang, Fuzheng Zhang, Zhongyuan Wang, and Heyan Huang. 2019. http://arxiv.org/abs/1905.07719 Earlier attention? aspect-aware LSTM for aspect sentiment analysis . CoRR, abs/1905.07719

2019 arXiv
[57]

Hu Xu, Bing Liu, Lei Shu, and Philip S. Yu. 2019. http://arxiv.org/abs/1904.02232 BERT post-training for review reading comprehension and aspect-based sentiment analysis . CoRR, abs/1904.02232

2019 arXiv
[58]

Weidi Xu and Ying Tan. 2018. http://arxiv.org/abs/1810.10437 Semi-supervised target-level sentiment analysis via variational autoencoder . CoRR, abs/1810.10437

2018 arXiv
[59]

Wei Xue and Tao Li. 2018. http://aclweb.org/anthology/P18-1234 Aspect based sentiment analysis with gated convolutional networks . In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2514--2523. Association ...

2018
[60]

Chao Yang, Hefeng Zhang, Bin Jiang, and Keqin Li. 2019. https://doi.org/10.1016/j.ipm.2018.12.004 Aspect-based sentiment analysis with alternating coattention networks . Information Processing and Management, 56:463--478

2019 doi
[61]

Liwen Zhang, Kewei Tu, and Yue Zhang. 2019. https://www.aclweb.org/anthology/P19-1457 Latent variable sentiment grammar . In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4642--4651, Florence, Italy. Association for Computationa...

2019
[62]

Yuan Zhang and Yue Zhang. 2019. https://www.aclweb.org/anthology/P19-1342 Tree communication models for sentiment analysis . In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3518--3527, Florence, Italy. Association for Computati...

2019
[63]

Peisong Zhu and Tieyun Qian. 2018. http://aclweb.org/anthology/C18-1092 Enhanced aspect level sentiment classification with auxiliary memory . In Proceedings of the 27th International Conference on Computational Linguistics, pages 1077--1087. Association for Computational Linguistics

2018

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

URL: " 'urlintro :=

ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

Md Shad Akhtar, Dushyant Chauhan, Deepanway Ghosal, Soujanya Poria, Asif Ekbal, and Pushpak Bhattacharyya. 2019. https://doi.org/10.18653/v1/N19-1034 Multi-task learning for multi-modal emotion recognition and sentiment analysis . In Proceedings of the 2019 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human L...

work page doi:10.18653/v1/n19-1034 2019

[4] [4]

Stefanos Angelidis and Mirella Lapata. 2018. http://arxiv.org/abs/1808.08858 Summarizing opinions: Aspect extraction meets sentiment prediction and they are both weakly supervised . CoRR, abs/1808.08858

arXiv 2018

[5] [5]

Lingxian Bao, Patrik Lambert, and Toni Badia. 2019. https://www.aclweb.org/anthology/P19-2035 Attention and lexicon regularized LSTM for aspect-based sentiment analysis . In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 253--259, Florence, Italy. Association for Computational Linguistics

work page 2019

[6] [6]

Chen , R

F. Chen , R. Ji , J. Su , D. Cao , and Y. Gao . 2018. https://doi.org/10.1109/TMM.2017.2757769 Predicting microblog sentiments via weakly supervised multimodal deep learning . IEEE Transactions on Multimedia, 20(4):997--1007

work page arXiv 2018

[7] [7]

Peng Chen, Zhongqian Sun, Lidong Bing, and Wei Yang. 2017. https://doi.org/10.18653/v1/D17-1047 Recurrent attention network on memory for aspect sentiment analysis . In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 452--461. Association for Computational Linguistics

doi:10.18653/v1/d17-1047 2017

[8] [8]

Zhuang Chen and Tieyun Qian. 2019. https://www.aclweb.org/anthology/P19-1052 Transfer capsule network for aspect level sentiment classification . In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 547--556, Florence, Italy. Association for Computational Linguistics

work page 2019