Sentiment Analysis on Streaming User Reviews via Dual-Channel Dynamic Graph Neural Network
Authors: Xin Zhang
Linhai Zhang
Deyu Zhou
Venue: EMNLP 2023 Main (2023)
Keywords: Sentiment Analysis; Streaming User Reviews; Dynamic Graph Neural Network; Online Review Websites
TL;DR: Recapturing the Importance of Time: A Sentiment Analysis Task Focusing on Modeling User and Product Temporal Dynamics for Streaming User Reviews

Abstract: Sentiment analysis on user reviews has achieved great success thanks to the rapid growth of deep learning techniques. The large number of online streaming reviews also provides the opportunity to model temporal dynamics for users and products on the timeline. However, existing methods model users and products in the real world based on a static assumption and neglect their time-varying characteristics. In this paper, we present DC-DGNN, a dual-channel framework based on a dynamic graph neural network (DGNN) that models temporal user and product dynamics for sentiment analysis. Specifically, a dual-channel text encoder is employed to extract current local and global contexts from review documents for users and products. Moreover, user review streams are integrated into the dynamic graph neural network by treating users and products as nodes and reviews as new edges. Node representations are dynamically updated along with the evolution of the dynamic graph and used for the final score prediction. Experimental results on five real-world datasets demonstrate the superiority of the proposed method.

Sentiment Analysis on Streaming User Reviews via Dual-Channel Dynamic Graph Neural Network
Xin Zhang Linhai Zhang Deyu Zhou"
School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, China (zhangxin, Izhang472, d.zhou) | @seu.edu.cn
Abstract
Positive words with gradually negative ratings
Negative words with
gradually positive ratings
Sentiment analysis on user reviews has achieved great success thanks to the rapid growth of deep learning techniques. The large number of online streaming reviews also pro- vides the opportunity to model temporal dy- namics for users and products on the timeline. However, existing methods model users and products in the real world based on static as- sumption and neglect their time-varying, charac- teristics. In this paper, we present DC-DGNN, a dual-channel framework based dynamic graph neural network that models temporal user and product dynamics for sentiment analysis. Specifically, dual-channel text encoder is em- ployed to extract current local and global con- texts from review documents for users and prod- ucts. Moreover, user review streams are inte- grated into the dynamic graph neural network by treating users and products as nodes and reviews as new edges. Node representations are dynamically updated along with the evo- lution of the dynamic graph and used for the final prediction. Experimental results on five real-world datasets demonstrate the superiority of the proposed method.
Re 111
04 20, 2015 * l
Reiew 111
0n 01, 2016 deledeeeee
really encoyed this book. The flow was very good the writing well done. I'm also glad that majority of the book was set years after the "dark times", in which crime nearly all-consuriing
read his latest book, Fool Me Once, only dislkedd thoughht fast-
was terrible. written paced style that makes a very quick read. Unfortunstely that's the one and only good thing can say about
months
months
Rio ow 122
08 26, 2015 sk
liked nostalgic tone and the style of writing. The characters felt much part of world they were set
Relew 82
09 03, 2016 de
Slow, steady boring. Very much unlike Coben bock. was very diseppointed but others think this a wonderful novel.
why
month
09 2015 * de teste Iws surprised that liked this book much did. listened the audio version Frances Fuller, her reading speed was really good; like fast
Retew N3
[1 month
Relew 83
10 03, 2016 *oook
This wasn't one of Harlan Coben's great stories,
main character was
uniliable. There was one in the entire story! could associate with.
An illustration of Streaming Reviews from a user to product. The left part indicates example of user who drafts the reviews with the positive words but scores lower with time, The right part indicates an example for product that receives reviews with negative words but is scored higher with time.
Figure 1:
Ma et al., 2017; Dou, 2017). Later, the framework of dual model with text was applicd to learn sep- arated representations for user and product (Long et al., 2018; 8; Lyu et al., 2020). Studies have shown that users and products often do not appear as inde- pendent individuals and exhibit shared preferences and social interactions (Kim and Srivastava, 2007; Mittal al., 2022). Therefore, recent attempts improve the embedding quality by implicitly in- corporating user-user or user-product relationships (Amplayo et al., 2018; Wen et al., 2020; Zhou et al., 2021c,b).
1 Introduction
Sentiment analysis on user reviews, inferring the overall sentiment polarity (c.g. 1-5 stars on the review site Amazon) of a user-written review docu- ment for a product, has gained popularity with the rapid growth of online review sites such as Ama- zon, Yelp, and IMDB. Compared other sentiment analysis tasks (Yang et al., 2019; Zhou et al., 2020, 2021a), not only the text itself but also the user and product information are crucial for the final rating score prediction.
However, all these aforementioned approaches assume that the user and product characteristics are static, which unreasonable in the ever-evolving real world. In fact, the users' preferences and the popularity of products are continuously changing over time. Figure illustrates a real case of shifts
For sentiment analysis on user reviews, early methods incororaae user and product embeddings by training randomly initialized embedding with the classifier (Tang al., 2015; Chen et al., 2016;
"Corresponding author.

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in user rating preference (user U) and product rat- ing preference (product P) in Amazon Book Re- view!. User U represents an example whose words remain positive while scoring gradually negative. Specifically, in the review on April 20, 2015, some positive words such as enjoyed, well done, good were used with a score of 5 given to the book. In the subsequent two reviews, user U still used posi- tive words, but the ratings were changed to 4 stars and 3 stars. It might indicate that user U's word habits remain consistent, while the scoring prefer- ence has shifted, suggesting a more cautious be- havior in rating. For product P. negative words such as disliked, boring, and unlikable are more likely to appear in the reviews during the display period. However, when analyzing rating changes, product P receives more positive ratings over time, indicating that has grown in popularity even with negative review documents. Based on the above observations, we assume that dynamic modeling with the temporal user and product dynamics is essential for sentiment analysis on user reviews in real-world changing contexts.
uct representations. Finally, the updated user and product representations along with the text repre- sentation are used for the current time score predic- tion.
We evaluate the proposed DC-DGNN on five self-built datasets. The experimental results show that DC-DGNN outperforms all existing state-of- the-art methods and the motivation for modeling the temporal dynamics of users and products is verified.
Related Work
2.1 Sentiment Analysis on User Reviews
Incorporating user and product information into models through reviews is the main idea of user view sentiment analysis methods. Tang et al. (2015) shows that modeling the reviewer as well as the product being reviewed is valuable for polarity pre- diction. Several works (Chen et al., 2016; Ma et al., 2017; Dou, 2017) utilize different semantic-level attentions to encode user and product information. Amplayo (2019) has also looked into the effects of various locations and methods for incorporating attributes into the model. Long al. (2018) first proposed a dual user and product memory network model learn their representation separately. Simi- larly, Lyu et al. (2020) stacks all available historical reviews separately for users and products to enrich their representations.
Therefore, in this paper, we propose a new study on sentiment analysis that focuses on streaming user reviews, which takes chronological reviews as a stream and aims to predict the rating score with the dynamic user and product representations. Since modeling pairwise relations between users and products in graph has been proven useful in sentiment analysis user reviews (Tan et al., 2011; Zhou al., 2021c), we provide an intuitive way to model streaming user reviews in dynamic graph and propose dual-channel dynamic graph neural network (DC-DGNN) to learn the temporal dynamics of users and products. To interconnect users and products while still preserving their spe- cific properties, DC-DGNN is designed with two dual-channel components based on first-separate- then-gather& strategy. First, dual-channel text coder is proposed to encode review documents into the local and global channels to learn separate user- related and product-related contexts respectively. Then, inspired by JODIE (Kumar et al., 2019), a dual-channel dynamic graph updating module is designed to aggregate information of the implicit user-review-produc bipartite graph. In this way, dynamic information flows smoothly from mo- ment to the next with the evolution of the dynamic graph and is integrated into current user and prod-
Recently, to exploit more knowledge from user- user user-product relations, Amplayo et al. (2018) introduces shared vectors that are con- structed from similar users/products to address the cold start problem. Yuan et al. (2019) creates in- ferred representations from representative users or products to use the inherent correlation between users or products. Wen et al. (2020) aggregates doc- uments written by similar users toward the same product to improve classification accuracy. Zhou et al. (2021b) applies group-wise model to use group information to supplement user information. However, none of the above works considered the critical factor of temporal information the real world. In fact, the characteristics of users and prod- uets are changing over time. Hence, relying solely on static modeling is not enough to capture their dynamic attributes.
2.2 Dynamic Graph Neural Network
Previous graph representation lcarning mainly fo- cused on static graphs with a predefined number of
Ihttps: //t////nnnanaaa/a thub. io/ mwwazzonninde...

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Embedding Database for Time
Em Embeccinz Database Time
p(t)
Pir
Encoding
User Encoding
Product Encoding
Updote
User Part
Prediction
Next Item
RAN Cell
u(t
u(t + An)
D(t
A.
U(C)
Projection Module
Item Part
p[L
Update Module
Dual Channel Text Encoding
Dual-Channel Dynamic Graph Updating
Figure 2: The architecture of Dual-Channel Dynamic Graph Neural Network (DC-DGNN). The dynamic graph evolves through two steps: (1) the review document is sent to Dual Channel Text Encading for Text Encoding. where text representation is encoded into user-related and product-related contexts; (2) the separated contexts are fed into User Part and Product Part Dual-Chanur Dynamic Graph Updating for User Encoding and Product Encoding, respectively.
nodes and edges. Real-world graphs, on the other hand, typically change over time as their graph structures change with the nodes and edges coming in disappearing. In order to tackle real-world situations, Continuous Time Dynamic Graph Neu- ral Network (Nguyen et al., 2018: Kumar al., 2019) and Discrete Time Dynamic Graph Neural Network (Sankar et al., 2020; Yang et al., 2021) for time dynamic graph representation learning have been proposed. However, despite its potential in modeling real-world graph integration and dynam- ies, previous studies only focus on its application in the fields of social networks, citation graphs, traffic networks, etc., while research sentiment analysis still remains blank.
the important mathematical notations used through- out the paper.
We propose Dual-Channel Dynamic Graph Neural Network (DC-DGNN) to model the dy- namics of the streaming review data, shown in Figure 2, which takes sequence of reviews input and integrates the review information into users and products through two dual-channel com- ponents. More concretely, for a document written by user, dual-channel text encoding component is first applied to encode text into user-related and product-related channels. In addition, a dual chan- nel dynamic graph updating component is proposed to integrate new review information into users and products. Finally, by concatenating the original text representation, the updated user representation as well as the updated product representation and feeding them into a prediction layer, we can obtain the dynamic scoring result for the current moment. The details of each component are discussed as follows.
3 Methodology
sentiment analysis on streaming user reviews, re- views are sorted in chronological order in the form of E = (E ".i....,TT},, Each of them is denoted as E, E-1(,, Pi, di), where f; is timestamp for the review d4. ue is the user who wrote the review d, and p, is the product being reviewed. The task aims predict the user's rating y towards the product under current condition &, based on the historical information {E,,...,& & }. and learns mapping function between user's rating y and condition &,, denoted as = (E,
3.1 Dual Channel Text Encoding
Earlicr research has highlighted the necessity differentiate between users and products cre- distinct representations (Ji et al., 2020). We consider that the global information of the text cor- responds to the user, while the local information corresponds to the product. Hence, we adopt a
Table lists

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Notations
Descriptions
formulas follows:
&,
The i-th review
E.
Database for users
Depth.convi
Σ
[sotmmax
w
Ep
Database for products
ii
(4)
И
Text embedding of the review document
j=1
Hy
User-related context
Xic)
X (イ)
Hy
Product-related context
u(t)
User's embedding time
where the output corresponds to the calculation results of the document's i-th element of output channel c. In the end, the weight of different con- volution cells is automatically selected through a gating mechanism for the product's local context:
P(t)
Product's embedding time User's embedding before time
u(r)
((()
Product's embedding before time I
u(t" A.)
Projected user embedding
p(t + Ap)
Projected product emhedding
p(t A.))
Predicted user's next product embedding
Hp = Σ exp(
12
Table I: Important notations and descriptions
1=1 EJ=1 (ap) Conv (H)
(5)
3.2 Dual Channel Dynamic Graph Updating
parallel multi-scale representation learning method called MUSE (Zhao et al., 2019) to encode text into global and local channels for users and prod- uets respectively. We first embed the document d 11 I22 ... .r through BERT (Devlin et al., 2019) to get its pretrained word embeddings:
Our proposed dual-channel dynamic graph updat- ing component is primarily inspired by JODIE (Ku- mar et al., 2019), with adaptive adjustments and designs specially made for sentiment analysis on streaming user reviews.
Given set of reviews with N users and M products, we first create two embedding look- up tables for both users and products as E. _I,....; uN] and E, = Pp,.... pPr]. which also act as a current representation storage database. At time 0, each user u and product in the database is initialized randomly from a uniform distribu- tion into an r-dimensional vector u(0) E IR' and p(0) E I". When new input arrives, the represen- tation stored in the database is taken out as u(t") and p(t and added the updating process. Af- ter this, the updated information will be renewed to the database as utt) and pt)). Note that we only maintain the most recent representation the users and products in these two databases.
h1, h2, h,] = BERT (11, "2," ". ..) (1)
where hy is d-dimensional feature vector for rep- resenting corresponding tokens z;. We denote text representation as И. Then we follow the subse- quent steps to obtain global and local context.
Channel 1: Self-Artention Mechanism for User's Global Context. We first project the document rep- resentation H into into three parts, key K, query Q, and value V. The global context for users then obtained by means of a self-attention mechanism:
QuKT
Ha = softmax
(2)
The entire updating process is designed as a dual-channel structure of user-part and product- part, where the user part contains a projection mod- ule and an updating module, and the product part only has an updating module.
ddu
where Qui Ku, Vu are transformed from H through the linear layer.
Convolution for Product's Local Context. DynamicConv (Wu et al., 2019) is ap- plied here, whose each convolution sub-module contains multiple cells with different kernel sizes to capture different-range features. The output of the convolution cell with kemel size is:
Channel 2:
User Part. The first part for the user is Projec- tion Module to process temporal projections. We assume that user preference is continuously shifting even when there are no review actions. Therefore, for user embeddings after the elapsed time A.. we perform the following projection function:
Conv (H) Depth Woutt
Wou
u(t + A,)= (1 + w) * u(t
(6)
where w R" is a time-context vector converted from A,, and the larger A, is, the more the pro- jected embedding vector differs from the input em- bedding vector.
where V,, is transformed from by a shared projec- tion with Channel 1, and cepth.covvy (V)) ) is a tra- ditional depthwise convolutional function, which

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Later, Next Product Prediction (NIP) cell. which predicts the product that the user is likely to review, is proposed to enhance the representation the user:
where WP E R ( w Rxx and by IR' are parameters of product's update RNN cell.
3.3 Training
P(L + + - Wiu(t + An)
In order to ensure the quality of user and product embeddings, we apply the L2 loss between t and t for users and products to prevent sudden changes in continuous time:
(7)
where p- (1-) is product embedding before time corresponding to the product from u's previous review, and Wi E R"xr, W2 R'x" and B R are trainable parameters in linear layer.
Liemooth = =llu(t)-u(t7)
-u()- (t
(11)
|ID(1)) p(t))12
(12)
For the NIP unit, our aim is to minimize the difference between the predicted product embed- ding p(( + A.) and the real product embedding p(t + Ap), where Ap is the time difference be- tween the current review and the last review for product p. which is different from A.. Since we assume that the products do not change during the time interval, here p(t + Ap) equals p(t"). Thus, the loss function can be represented as:
where u(!) and u(t represent the user's previ- ous and current representation respectively, while p corresponds to the product.
For score prediction at time t, we calculate cross- entropy loss between the predicted score ÿ and true score y:
N
Lscore
ylog( (T)
(13)
Lnip
Σ
|ID(t + + - pP (8)
where ÿ - MLP (up((((())()))
(N...d)EE
Finally, our training objective can be formulated as follows:
where we use the 1.2 loss function to push the predicted product representation closer to the true product representation.
I. = AI + A2
The second part for the user is an Update Mod- ulc, implemented based on an RNN cell, which generates the updated representation of the user after this review. It takes the projected user embed- ding u(t + Au), the previous product embedding p(t),, and the user's time interval Au as input, then integrates them into the current user represen- tation:
(14)
+ >gILvsmont + dg
where Å1. A2 and λ3 as well as X4 are tradcoff pa- rameters for each loss.
4 Experiments
We construct 5 datasets for sentiment analysis on streaming user reviews to evaluate the performance of our model and promote the development of this research. We first collect S-core data from the Ama- zon Reviews, which are built by McAuley et al. (2015) and Ni et al. (2019) in variety of cate- gories to ensure the diversity of our datasets. The former dataset we refer covers the period from May 1996 to July 2014, and the latter ranges from May 1996 to Oct 2018. The statistics of the datasets are shown in Table 2. For more construction details, see the Appendix.
u (t) tanh (WT" (WT"ttWtTt(() (p(tT) @ Hw)
(9)
+ Wluu(t Au) bba)
where WP C B (++1+))xr xr WH C Rexe and bu E RR are parameters of user's update RNN cell. Through these two modules, the user's repre- sentation flows smoothly from tr to t.
Product Part. The product part is similar to the user side, except that there no projection module, as we consider the product to be static for the duration. In contrast to the user part, the positions of u(t - + A.) and p(t)) are switched, and the context used for product updating is Hp:
We adopt BERT (base-uncased) (Devlin et al., 2019) the pretrained encoder for texts. For user and product embeddings, we explore in dimen- sions [8, 16, 32, 64, 128, 256). The batch size is 8, and the learning rate is 3c-5, the number of epochs A1, Au, A and X are all set to 1. The dropout
(()) tanh (aaT(u(f((( + + 11 +W?p+ +bpp
(10)

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Catagory
Total Num
User Num
Product Num
Avg r/u
Avg r/p
Test Avg r/u
Test Avg r/p
Books
25
500
497
50.328
50.632
52.019
56.066
Grocery and Gourmet Food
9725
500
500
19.45
19.45
20.456
24.088
Kindle Store
11742
500
488
23.484
24.061
24.632
28.373
Sports and Outdoors
6002
500
493
12.004
12.174
12.278
15.537
reviews CDs and Vinyl
19583
300
500
39.166
39.166
44.819
41.610
Table 2: Statistical information of our self-constructed datasets, Avg r/u' means the average associated reviews number for users, and 'Avg r/p' means the average associated reviews number for products. Test corresponding to the test set.
of MUSE is set to 0.1, and three different kernel size of [1, 3, 3, 5] is applied for deepwise convolution. modifications the JODIE struc- ture, see the Appendix. We formalize the score prediction task as a classification problem, and the following seven evaluation indicators are used to evaluate our model: Accuracy, Precision, Recall, FI, MSE, RMSE, and MAE. For the split of the training set and test set, we adopt ratio of 1.
2021b) exploits group information to supple- ment user information by group-wiss model, which is designed regression task with bias.
For more
4.2 Main Results
The experimental results on the datasets from May 1996 to October 2018 are shown in the first four rows of Table 3, and the dataset from May 1996 to July 2014 is shown in the last row of Table 3. It can be observed that:
4.1 Baseline Methods
As the task sentiment analysis on streaming user reviews has not yet been investigated, we create two different baselines to contrast with our DC- DGNN method:
1) On all five datasets, the performance of the Bert-hased model (Bert-Sequence, IUPC, and our DC-DGNN) is better than that of the Glove-based model (BiLSTM+Att, NGSAM, and CHIM), which proves that high-quality feature extractor still necessity for senti- ment analysis.
Text-based model: The Bert-Sequence model, Bert Model transformer (Devlin et al., 2019) with a sequence classification head on top, which is frequent winner in clas- sification tasks, still applied here as a very competitive baseline. Similarly, the structure of the combination of Bi-directional LSTM with Attention (BiLSTM+Att) is also used as another baseline.
2) Our DC-DGNN model outperforms all other baselines on datasets, confirming the supe- riority of DC-DGNN in modeling user and product temporal dynamics. Meanwhile, com- pared to JODIE, our DC-DGNN has signifi- cant improvements, which shows that we have successfully adapted the dynamic graph struc- ture our sentiment analysis task.
User and Product- model: JODIE (Ku- mar et al., 2019) applies coupled recurrent neural network model learn embedding tra- jectories of users and items, which has the ability to predict state change in users of the recommendation system. Here we use it as a baseline for score prediction. Due to its t-batch strategy, only the regression task can be implemented here. CHIM (Amplayo, 2019) adopts a chunk-wise matrix represen- tation for user/product attributes and injects user/product information in different locations. IUPC (Lyu al., 2020) uses dual channel modeling approach similar to ours by stacking text information into user and product through multi-hea head attention. NGSAM (Zhou et al.,
3) Comparing the results on the first four datasets with the last dataset, we observe that our model shows convincible and considerable performance when datasets have relatively longer timespan, which indicates that our DC- DGNN has the potential model on more real-world ever-increasing streaming datasets.
4.3 Ablation Study
In order verify the impact of the modules pro- posed in this paper, ablation experiments were de- signed as follows: 1) w/o DCTE means removing the Dual Channel Text Encoding (DCTE) structure and directly updating the user and product with

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Criteria
Dataset
Method
(ccuracy(t)
Procision())
Recall(")
Fi(f)
MSE(L)
RMSE()
MAE(U
BiI
0.5793
0,4219
0.3528
0.3458
0.6726
0.8201
0.4970
Bert Bert- Sequence
0.6687
0.6032
0.4941
0.5269
0.4203
0.6483
0.3584
JODIE
17.6734
4.2040
4.2035
Books
NGSAM
0,7027
0.8383
0.6401
CHIM
0.6293
0.5338
0.4310
0.4557
0,6293
0.7933
0.4466
IUPC
0.6722
0.6237
0,5130
0.5473
0.4168
0.6456
0.3548
DC-DGNN
0.6814
0.6086
0.5363
0.5627
0.4044
0.6360
0.3449
BiLST
0.6814
0.3925
0.3755
0,3816
0.8243
0.9079
0.4543
Bert- Sert-Sequence
0.7235
0.5672
0,4032
0,4202
0.5036
0.7096
0,3453
Grocery
JODIE
19.5945
4,4266
4,4246
Gournet
Food Food
NGSAM
0.6628
0.8141
0,5462
CHIM
0.7050
0.5425
0.4514
0.4660
0.7328
0.8560
0,4080
IUPC
0.7235
0.3879
0,3666
0,3563
0.5971
0.7727
0.3669
DC-DGNN
0.7451
0.6207
0.5191
0.5021
0.4892
0.6994
0.3227
MiL
0.5804
0.3325
0.3561
0.3337
0.7864
0.8868
0.5226
Bert-Sequence
0.6511
0.5540
0.4897
0.5086
0.4233
0.6504
0.3719
JODIE
17. 17,4456
4.1768
4.1761
Kindle_Ston
NGSAM
0.5454
0.7385
0.5515
CHIM
0.6485
0.4535
0,3986
0.4035
0,6026
0.7762
0.4238
IUPC
0.6545
0,5672
0.4994
0,5198
0.4383
0.6620
0.3719
DC-DGNN
0.6664
0,5912
0.5157
0.5370
0.3991
0.6318
0.3532
BiLSTM+Ant
0.6689
0.1843
0,2016
0.1637
1.2396
1.1134
0.5441
Bert-Sequence
0.6872
0.3879
0.3447
0.3545
0.5391
0.7342
0.3794
Sports and
JODIE
19.8610
4.4566
4.4558
Outdoors
NGSAM
0,7356
0.8577
0,5840
CHIM
0.6905
0,4392
0.3109
0.3341
0.9967
0.9983
0.4742
IUPC
0.6872
0.4034
0,3713
0.3791
0.5541
0.7444
0.3844
DC-DGNN
0.7038
0,4227
0,3950
0.4063
0.5025
0.7089
0.3561
M+
0.6605
0.4227
0.3988
0.3939
0,8474
0.9205
0.4655
Bert-Sqquence
0.7182
0.5635
0.5218
0.5396
0.4436
0.6660
0.3252
reviews_ CDs
JODIE
18.5957
4.3123
4.3091
_and_ Vinyl
NGSAM
0,7320
0.8556
CHIM
0,7009
0,5340
0,4803
0,4912
0,5365
0.7325
0,3640
IUPC
0.7070
0.5363
0.5056
0.5177
0.4273
0.6537
0.3313
DC-DGNN 0.7243
0.5691
0,5435
0,5542
0,4084
0.6390
0.3134
Table Main experimental results our DC-DGNN model and comparison with previous works on datasets the May 1996- Oct 2018 time period (first datasets) and May 1996 July 2014 time period (last dataset). J indicates the smaller the metrics, the better the method, while 1 indicates the larger the metrics, the better the method. The score marked bold means the best performance among all the methods.
Grocery _nnd_______ Gourmet Food
Sports_and Outdoors
reviews CDs and_ Vinyl
Books
Kindle_Store
RMSEC)
MAE())
RMSE(L)
MAE())
RMSE(L
MAE())
RMSE()
MAB(L)
RMSE(J
MAB())
w/o DCTE
0.6553
0.3643
0.6648
0.2980
0.6451
0,3566
0.7664
0,3977
0.6406
0,3175
w/o user&product
0.6553
0.3651
0.7096
0.3124
0.6588
0.3745
0.7477
0,3993
0.6446
0.3267
DC-DGNN
0.6360
0,3449
0.6994
0.3227
0.6318
0,3532
0.7089
0.3561
0,6390
0,3134
Table 4: Compare the results under the original structure with the results of w/o DCTE and w/o user&produet.
the BERT cls representation; 2) w/o user&product removes user and product information, and only completes score prediction through dynamically learned text representation. As shown in Table w/o DCTE and w/o user&product all cause a per- formance drop on each dataset except for Gro- cery_and__ mnd_GGurmet___ Food, where its text content is relatively short, and MUSE in DCTE generally has advantages in encoding long texts rather than short texts. We also find that both w/o DCTE
and user&product result in significant drops on Sports_and annd_Outdoor, indicating that even with relatively sparse user/product information but a long timespan, DC-DGNN's dual modeling of user and product dynamics still plays crucial role in score prediction.
4.4 Variants of DC-DGNN Structure
Discussion of User and Product Embedding Di- mensions. Different datasets have their own char-

---

Buttercup, beautiful young furm girl and Wesley, a farm hand who fall in love with each other. But the country of Florin there reposes evil in
The
Anyone interested geure will most likely enjoy his books, several of which have been made into films. I am particularly big fin such books, but have read a few; including several of earlier ones, Four interesting and entertaining, but deeply memningful. I recommendd that you reau which they writlen and continue for long you find lbbm interesling.
decided to Paula Hawkins The Girl the Teain because il received quile good critical reviews. But after about 50 pages found it to be depressing and difficult to follow since the story keeps switching among various people
CHIM 5
BERT
Prince Humpemick and his aide Count Rugen. Many people will have seen the movie as well and thus know the basic plot. Still they will find delight in reading the book.
dates. skipped and read the last pages just see how it cnded. Rachel is diverced from Tom and is depressed an alcobolic, the plot. unfolds the characters interact with each other negative ways to the iragic ending.
end
IUPC
JODIE
novels
OURS
09 26, 2016
01 27, 2017
02 10, 2017
?
Figure 3: The case study (grey indicates neutral expression, red indicates positive expression, and green indicates negative expression).
acteristics in distribution and domain, so it is unrea- sonable to set the same embedding dimension for modeling all datasets. To this end, we conduct dimension exploration experiment to compare the impact of different dimension sizes on 5 datasets. Overall in Figure 4, we can see that the better re- sults (i.e., the darker the blue color) are basically on the right side of the graph, indicating that in most cases, it's appropriate to set relatively large dimension. This is because our dataset is basically at the level of ten thousand, with relatively abun- dant data, and modeling with large dimensions is beneficial. However, the largest is not always the best when some datasets are relatively small. For example, Sports_and Outdoors achieves the best results when the dimension is 64.
Books
Grosery_ans__cummeee
Sponia Outtorrs
Figure 5: RMSE comparison of different text processing methods in DCTE
encoding at position 0 of the global Hy and local representations Hp respectively for updating users and products; mean averages the representation of the entire dimension 1 of H,, and Hp; max con- ducts max max_ppoolld on the entire dimension 1. For the sake of fairness, we compare their performance with both the user and product dimensions 128. Figure 5 shows that, in general, cls can achieve relatively good results. However, in specific, the method corresponding to the best results still de- pends on the characteristics of different datasets.
0.76
Books
0.646
0.648
0.647
0.643
0.636
0.647
0.74
Grocwyy.nd, Gourmet Fond
Kindle Store
0.6h0
0.644
0.861
0.5.2
10
0.859
11 8.00
0.68
Sports, and Outdoors
0.66
reviews CDs and Vinyl
0.647
0.650
0.857
0.647
0.646
0.630
0.64
de
dim 16
dm
dm 64
dim
250
4.5
Case Study
Figure 4: The visualize RMSE scores by setting the user
Figure 3 shows user's reviews sampled from the Books dataset. The goal is to predict the rating score from the same user of the review on February 10, 2017. CHIM makes the worst prediction that differs the most from the actual 1-star. This is be- cause CHIM focuses on attribute incorporation and cannot obtain user- or product-biased sentiment in- formation. IUPC and BERT get the same results, indicating that IUPC cannot satisfy the need for user dynamics modeling when the user characteris- tie is complex. JODIE's result also indicates that its original structure is not an appropriate setting,
and product dimensions to [8, 16, 32, 64, 128, 256) on different datasets
Variants of Processing Text Information for DCTE. After obtaining global and local contexts through DCTE, it has additional dimension re- lated sequence length compared user and prod- uct representation. To align them, we have multiple processing methods. Other than the cls result re- ported in the main experiment, we also tried two different methods: mean and max. cls takes the

---

explained in detail in the Appendix. Our model, on the contrary, can accurately capture the user's constantly negative scoring trend when the three expressions are very close and predict score of I, which shows the importance of dynamic modeling.
Limitations
Although our model has shown excellent perfor- mance in sentiment analysis on streaming user re- views, we still believe it has some limitations:
Comparing datasets with longer timespan and shorter timespan, we find that improvement is not noticeable for the datasets with shorter timespan, which is limitation for analysis only with short-term data. At the same time, our DC-DGNN model is also not friendly for datasets with sparse user/product information.
4.6 Computational Efficiency
In this section, we will discuss the time efficiency comparison of our proposed DC-DGNN and other models. We conduct time test to compare model's performance with that of TUPC and CHIM, which also learn user and product embeddings si- multaneously. As shown in Table 5, DC-DGNN demonstrates better computational efficiency on most datasets. This can be attributed to the contin- uous storage of updated embeddings in a database, in contrast to previous methods that train two sepa- rate embedding instances. As result, our method's time complexity is O(L e) + O(L H d), which is reduction compared to the O(D. L- H Hdd) com- plexity of other methods. Furthermore, the time advantage becomes even more obvious as any of these dimensions increases. While CHIM, which relies on GloVe (Pennington et al., 2014) encoding, may show faster processing on specific datasets, it requires larger number of training epochs.
In early experimental attempts, for the struc- ture built directly from JODIE, we found sig- nificant differences in prediction performance at different times. When making predictions on data with relatively recent time, the per- formance is great, and when the time is farther away, the performance is sharply decreased.
The current structure we proposed considers forward integration and ignores backpropa- gation. In fact, the addition of subsequent reviews will also have an impact on past node representation. At the same time, we also ignore the high-order correlations between user-user and product- product.. For example, user and user can be connected through a second-order homogeneous graph. The above- mentioned more refined design in graph updat- ing may be our future improvement direction.
Calegory
DC-DGNN
IUPC
CHIM
Books
9,3172
12.1095
21.5937
Grocery_and and Gourmet_
3.3225
3.7920
2.2807
Kindle_Store
4,1851
4.2269
4.7659
Sports_and aatiianddddaii Outdoors
2.0984
2.1240
1,2126
reviews_ CDr_and___ Viny!
7.1875
7.7507
14.4805
Acknowledgement
Table 5: Comparison of Training Time (minutes/epoch)
The authors would like to thank the anonymous reviewers for their insightful comments. This work is funded by the National Natural Science Founda- tion of China (62176053). This work is supported by the Big Data Computing Center of Southeast University.
5
Conclusion
In this paper, we present novel research senti- ment analysis on streaming user reviews and pro- pose dual-chamned dynamic graph neural network, namely DC-DGNN, to model the temporal dynam- ics of users and products. DC-DGNN dynamically updates user and product representations through the dual-channel evolution of the graph. On our 5 cofffconstr datasets, by comprehensive evalua- tions and ablation study, we confirm the superiority of our DC-DGNN and the impact of its modules. Through additional analytical experiments, ther demonstrate the importance of modeling user and product dynamics, hence verifying the conjec- ture in this paper.
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A Experiment Details
Additionally, JODIE has a problem with gra- dients vanishing during testing, As JODIE was designed for recommendation systems, it keeps gradients propagation during testing, which is not ideal for our sentiment analysis task. To solve this, we propose a strategy that saves and updates the user and product representations in the databases during training. And during the testing phase, we only take the representations out of the database without updating them.
Dataset construct details. Firstly, the data was subjected a cleaning operation where we re- moved duplicates, as well as the data where their texts are empty, and then removed special charac- ters from the texts. Later, the processing steps to build the datasets for sentiment analysis on stream- ing user reviews
follows:
Step1: Sort users by the number of reviews they have made, then filter the data contain- ing the top 500 users from the original data according to their review counts.
In JODIE, there is also small experimental problem. We find that its static embedding setting conflicts with successful training. JODIE sets one- hot vectors as static embedding for each user and item in rude way, which is not friendly to the situation where there are large number of users and items, and will directly cause calculation over- load and make impossible train. Therefore, in this paper, we deal with this problem by directly abandoning static embedding.
For the data obtained in the first step, sort the products according to the number of reviews they received, and filter the data con- taining the top 500 products.
Step2:
Step3: Normalise the data. For time process- ing, convert it to timestamp format and sub- tract the smallest timestamp the dataset. For users and products, map the unique identifier to a numeric number.
B Supplement Experimental Results
Based on the unified dynamic graph library imple- mented by Yu et al. (2023), present in Figure 6 comparative analysis of our DC-DGNN along- side several other dynamic graph models. CAWN (Wang et al., 2021) leverages causal anonymous walks and recurrent neural networks to extract node representations by exploring network dynamics and causality. DyGFormer (Yu et al., 2023) utilizes neighbor co-occurrence encoding scheme and a patching technique to capture long-term temporal dependencies. Graph (Cong et al., 2023) incorporates a fixed-time encoding function into a link encoder, and employs a node encoder with neighbor mean- mean-poolin pooling for feature summarization. From the results, it is obvious that their perfor- mance is actually on par with JODIE's, which is unsatisfactory. All of these models also rely on text information for training, similar to model. However, it is clear that they do not fully exploit the potential of review texts, resulting in subpar task performance, which further reflects the significance of our work.
Step4: Finally, the data are sorted by times- tamp fit in our chronological setting.
The reason why Text-based model is necessary here. Although many previous methods that incor- porate user and product information have shown strong potential, however, in some cases, they not only fail learn high-quality representations but have counterproductive effects on score prediction. For example, in some datasets, only a few products are widely reviewed, and the number of reviews received by different products shows an imbalance. In fact, this data imbalance is very common in the real world, and even if we try to avoid it when constructing datasets, it still exists to some extent. Therefore, in this paper, still take text-based model strong baselinc.
Experimental adjustments for adapting JODIE to sentiment analysis tasks. The origi- nal JODIE structure first divides the data by times- pan and then further divides the data by t-batch. T-batch is a strategy to prevent the overlapping of users and items in each batch. However, we believe this strategy may be inappropriate it disrupts the original time order, which contradicts the chrono- logical setup. To address this, we only divide the data into multiple time periods according times- pan and abandon the t-batch strategy to ensure the original order the data.

---

Criteria
Dataset
Method
Accursy(())
Precision(1 f
Recall(1)
F1(1)
MSE(()
RMSE())
MAE())
JODIE
0,4849
0.1927
0.2046
0.1409
1.5655
1,2512
0.7977
CAWN
0.3144
0.0629
0.2000
0.0957
0.9756
0.9877
0.7688
DyGFormer
0,4785
0.0957
0,2000
0.1295
1.5965
1.2635
0.8112
Books
GraphMixer
0.0201
0.0040
0.2000
0.0079
11.1066
3.3327
3.1888
DC-DGNN
0.6814
0.6086
0.5363
0.5627
0,4044
0.6360
0.3449
jodie
0.1942
0.0634
0,2116
0.0871
1.6637
1,2898
1,0323
CAWN
0.2080
0.0416
0.2000
0.0689
1.0748
1.0367
0.8703
Grocery and
DyGFormer
0.0302
0.0060
0,2000
0.0117
6.7165
2.5916
2,4585
Gourmet_Food
GraphMixer
0.0240
0.0048
0,2000
0.0094
12.5374
3,5408
3.4104
DC-DGNN
0.7451
0.6207
0.5191
0.5021
0.4892
0.6994
0.3227
JODIE
0.3589
0.1394
0.2005
0.1111
1.0006
1.0003
0.7416
CAWN
0.3606
0.0721
0,2000
0.9216
0,9600
0.7195
DyGFormer
0.0380
0.0076
0,2000
0.0147
5.5645
2.3589
2.2027
Sindle_Store
GraphMixer
0.0210
0,0042
0,2000
0.0082
10.8859
3.2994
3.1607
DC-DGNN
0.6664
0,5912
0,5157
0.5370
0.3991
0.6318
0,3532
JODIE
0.2427
0.1988
0.2065
0.0971
1.3662
1.1689
0,9224
CAWN
0,2056
0.0411
0,2000
0.0682
1.0911
1.0446
0.8733
Sports_ond and
DyGFormer
0.0189
0.0038
0,2000
0.0074
7,1000
2.6646
2,5622
Outdoors
GraphMixer
0.0300
0,0060
0,2000
0.0117
13. 1044
3.6200
3,5022
DC-DGNN
0,7038
0.4227
0,3950
0.4063
0.5025
0.7089
0,3561
JODIE
0.0000
0.0402
0.1557
0.0639
2,2033
1,4844
1.2033
CAWN
0.2491
0.0498
0,2000
0.0798
1.1668
1.0802
0.8652
reviews_ CDs
DyGFormer
0.5830
0,1
0,2000
0.1473
1.5650
1,2510
0.6991
and Vinyl
GraphMixer
0.0364
0,0073
0,2000
0.0141
11.9721
3,4601
3.3009
DC-DGNN 0.7243
0.5691
0.5435
0.5542
0.4084
0.6390
0,3134
Table 6: Supplement experimental results of our DC-DGNN model and comparison with previous dynamic graph works on datasets in the May 1996- Oct 2018 time period (first datasets) and May 1996 July 2014 time period (last dataset). J. indicates the smaller the metrics, the better the method, while indicates the larger the metrics, the better the method. The score marked as bold means the best performance among all the methods.


Reviews:
[{"review_id": "jaSd4TGYxZ", "reviewer": "Reviewer_XFAW", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the comments. The major concerns of the reviewer are answered below.\n\n> The paper does not discuss on the computational cost of the proposed approach. The sentiment analysis on user reviews usually requires fast computation due to a large volume of reviews in real services such as Amazon and Netflix. It will be good to include computation complexity or running time comparison.\n\nThe proposed approach is computational-efficient because  updated embeddings are continuously stored in a database, while previous methods rely on training two separate nn.Embedding. As a result, the time complexity of our method is O(L * e) + O(L * H * d), which is relatively smaller than O(D * L * H * d) of other methods. This time difference increases as any of these dimensions grows. We also conducted time tests, where only the IUPC and CHIM models concurrently learn user and product embeddings, serving as our points of comparison. As shown in the table below (measured in miniute/epoch), our model's performance is superior. While CHIM, which relies on GLove encoding, is faster on specific datasets, it requires more training epochs.\n\n|     | OURS | IUPC | CHIM |\n| --- | --- | --- | --- |\n| Books | 9.3172 | 12.1095 | 21.5937 |\n| Grocery_and_Gourmet_Food | 3.3225 | 3.7920 | 2.2807 |\n| Kindle_Store | 4.1851 | 4.2269 | 4.7659 |\n| Sports_and_Outdoors | 2.0984 | 2.1240 | 1.2126 |\n| reviews_CDs_and_Vinyl | 7.1875 | 7.7507 | 14.4805 |\n\n> The experiments are highly biased to the heavy users and items. In the data preprocessing, this work only considers 500 heavy users and items that are associated with many reviews. Thus, the experiment shows the effectiveness of the proposed model only on the heavy users and items, while most users and items do not have many reviews.\n\nAlthough we chose the top 500 users, according to the statistics of the datasets in Table 4 in the appendix, the avg r/u is not high on some datasets, such as only 12 on the Sports_and_Outdoors dataset, which is not considered as heavy users or items. Indeed, how to deal with low-resource users is a problem worthy of further exploration. We will study the model performance comparison between high-resource users and low-resource users later.\n\n> In the main experimental results (Table 2), the performance of JODIE is problematic. Its MAE is over 4, which is unusual with considering the Amazon datasets have 5-scale ratings. The paper has to discuss the issue to convince readers.\n\nThe original JODIE structure is designed to learn user and product embeddings in recommender systems, and its task design and framework design are different from ours. We observe that the user and product representations learned by JODIE are very similar to an unusually close degree, and we tend to think that it only learns the surface representation. The JODIE results we reported in the paper are based on the original implementation version of JODIE (without earlystop), and the latest JODIE (with earlystop) is based on the revised version of Yu et al. 2023. Updated results of JODIE are not as bad as we ran out (as follows table), but not good either.\n\n|     | accuracy | precision | recall | f1  | MSE | RMSE | MAE |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| Books | 0.4849 | 0.1927 | 0.2046 | 0.1409 | 1.5655 | 1.2512 | 0.7977 |\n| Grocery_and_Gourmet_Food | 0.1942 | 0.0634 | 0.2116 | 0.0871 | 1.6637 | 1.2898 | 1.0323 |\n| Kindle_Store | 0.3589 | 0.1394 | 0.2005 | 0.1111 | 1.0006 | 1.0003 | 0.7416 |\n| Sports_and_Outdoors | 0.2427 | 0.1988 | 0.2065 | 0.0971 | 1.3662 | 1.1689 | 0.9224 |\n| reviews_CDs_and_Vinyl | 0.1600 | 0.0402 | 0.1557 | 0.0639 | 2.2033 | 1.4844 | 1.2033 |", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. This work aims to address an interesting task, sentiment analysis on streaming user reviews, and proposes a suitable method to tackle the task. \n2. The classification accuracy of the proposed model is the highest compared to the numbers of baseline models including recent BERT-based models.\n3. The paper is well written so that it is easy-to-follow.", "reasons_to_reject": "1. ~~The paper does not discuss on the computational cost of the proposed approach. The sentiment analysis on user reviews usually requires fast computation due to a large volume of reviews in real services such as Amazon and Netflix. It will be good to include computation complexity or running time comparison.~~\n\n2. The experiments are highly biased to the heavy users and items. In the data preprocessing, this work only considers 500 heavy users and items that are associated with many reviews. Thus, the experiment shows the effectiveness of the proposed model only on the heavy users and items, while most users and items do not have many reviews. \n\n3. ~~In the main experimental results (Table 2), the performance of JODIE is problematic. Its MAE is over 4, which is unusual with considering the Amazon datasets have 5-scale ratings. The paper has to discuss the issue to convince readers.~~", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper proposes a sentiment classifier for streaming user reviews by designing dynamic graph neural network. The experimental results show the superiority of the proposed model.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Missing_References": "A recent BERT-based model for rating prediction (sentiment analysis) should be included in the related work. \n\nQiu, Z., Wu, X., Gao, J., & Fan, W. (2021). U-BERT: Pre-training User Representations for Improved Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 4320-4327."}}, {"review_id": "8A0Wuis4Vw", "reviewer": "Reviewer_tvcR", "summary": "", "questions": "Q1: Is it necessary to build complex models to capture user and product dynamics to infer the sentiment of the given text? Does this task focus more on detection rather than prediction?\n\nQ2: Why is only the regression performance reported in Table 3?\n\nQ3: When dealing with online scenarios, can DC-DGNN deal with unseen users and products?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the comments. The major concerns of the reviewer are answered below.\n\n> R1: The motivation of this paper is not convincing. Existing works usually model the users and products to make predictions, while the task studied in this paper falls into the detection category. According to Line 182, the task aims to predict the user's rating y given the current condition that contains the review document. As I understand, the review document is almost sufficient to determine the sentiment. And the BERT performance also acknowledges the importance of review documents. I don't think it is essential to build complex models to capture user and product dynamics in such a scenario.\n> \n> Q1: Is it necessary to build complex models to capture user and product dynamics to infer the sentiment of the given text? Does this task focus more on detection rather than prediction?\n\n**On the one hand, we found through analysis that the user and product representation learned by the previous work on the insufficient use of text information is surface.** These representations may perform well on ranking prediction tasks that are less difficult, but they perform poorly on more difficult classification tasks. Therefore, building a sophisticated way to learn more refined user and product representations is actually our real purpose, and it can be used for both prediction tasks (when there is no given text at the next moment) and detection tasks (when there is a given text at the next moment).\n\n**On the other hand, sentiment analysis of streaming user reviews is a very valuable problem.** Previous work on sentiment analysis did not distinguish temporal tags. In practical scenarios, when dealing with user reviews in 2019, it is unrealistic to rely on reviews in 2020 for model training. In fact, experiments also prove that when we switch the setting from shuffle to temporal, the performance of many previously excellent models has dropped significantly. For example, as shown in the table (accuracy), the IUPC model performs very well in the shuffle setting, but drops significantly in the temporal setting, emphasizing the need to study the sentiment analysis under the temporal setting.\n\n|     | IUPC-shuffled | IUPC-temporal |\n| --- | --- | --- |\n| reviews_CDs_and_Vinyl_5_top500_sorted | **0.7167** | **0.707** |\n| Grocery_and_Gourmet_Food_5_top500_sorted | **0.7636** | **0.7235** |\n| Sports_and_Outdoors_5_top500_sorted | **0.7155** | **0.6872** |\n\n> R2: The novelty of the model is limited. The main architecture comprises MUSE and JODIE with slight modifications.\n> \n> R2.1: It is unclear how MUSE can capture global and local information for users and products respectively. No corresponding results are provided.\n> \n> R2.2: The adaption of JODIE is questionable. In the original JODIE, the projection module is used for future prediction, while this paper takes it to update the representations of users and products. The design is unreasonable as I understand.\n\nIn fact, model transfer between different fields is not as simple as it seems. In this process, we spent a lot of time, from input to intermediate processing to final testing, all of which have undergone a lot of modifications. As is shown in the paper, the performance gap between the proposed method and JODIE is huge. During the experiments, we found that the gap between the recommendation system field and the NLP field is actually very large. **Eliminating this gap and applying the dynamic graph model to the streaming user review sentiment analysis is a big part of our work.** We are the first to effectively bridge the gap and successfully achieve the goal.\n\n- *Regarding the modification of JODIE*, in fact, we have also done a lot of analysis and changes. Although it may not be seen in the paper, in the actual code implementation, we almost replaced the entire implementation framework of JODIE, making it suitable for sentiment analysis in the NLP Task field. Throughout the work, we have conducted a detailed study of what the original paper describes and what the code implements. Since the user has no other information except the id tag, even if the word \"prediction\" is used for the projection module in the original JODIE, we tend to understand it as a \"simulation of the future trajectory\" rather than a real prediction. As a matter of fact, we have identified implementation errors in various other aspects of JODIE. For example, the training strategy mentioned in the appendix and numerous details regarding embedding updates. We have modified JODIE and improved it.\n  \n- *For the usage of MUSE*, we use such description: \"We consider that the global information of the text responds to the user, while the local information corresponds to the product.\". The most common explanation of MUSE is that it has a self-attention that captures global features and a depth-separable convolution for capturing local features, whereas \"the convolution concentrates on the full use of local information, while the self-attention focuses on capturing dependencies\" is recognized. In practice, when applying MUSE, we assume that, for users, the emphasis lies on comprehending the entire text, whereas for products, the focus is on specific details. The utilization of MUSE is motivated by our intention to employ an unsupervised method to create a distinction between user and product representations, aiming to address the inherent issue of overly similar representations present in the original JODIE model.\n  \n\nHence, the proposed method is not a simple combination of JODIE and MUSE, but a further development based on them with carefully design.\n\n> R3: Though extensive experiments are reported, the results cannot provide sufficient useful information to demonstrate the superiority of the proposed model except for Section 4.2. Incorporating more tasks or diving deeper into the model's performance of capturing user and product dynamics may help. An illustration of the user/product embedding's trajectory can also help.\n\nWe will include a demonstration figure of user/product embedding's trajectory in the paper:\n\n1. We will divide the timeline into three timespans, where each timespan contains a portion of negative users (positive text but low ratings), positive users (negative text but high ratings), and neutral users (others).\n2. We will extract user IDs for each of the three timespans using predefined rules, highlighting that these user IDs change across different timespans.\n3. Utilizing T-SNE visualization, we will showcase the user embeddings learned by our DC-DGNN across these three timespans. Notably, the user IDs belonging to the same cluster across different timespans will correspond to the user IDs extracted in step 2.\n\n> Q2: Why is only the regression performance reported in Table 3?\n\nSince we use a lot of space to show all the evaluation indicators in Table 2, in order to control the length of the paper, we choose to show only regression scores in Table 3.\n\n> Q3: When dealing with online scenarios, can DC-DGNN deal with unseen users and products?\n\nSince we don't know the more detailed portrait information of users and products, the way we learn dynamic user and product embeddings is identified by a unique id, which means that it cannot handle unseen users and products. In fact, limited by the additional portrait information, many works do the same. If there are multiple portrait information, the method of combining multiple characteristics to form embedding can be used to deal with the unseen situation.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "A1. Sentiment analysis of online streaming reviews has practical implications in various applications and is worth studying.\n\nA2: The model adopts effective existing modules to capture user and product dynamics and yields promising performance in the experiment part.\n\nA3: The authors compare their model with extensive baselines and provide an in-depth analysis of the proposed model.", "reasons_to_reject": "R1: The motivation of this paper is not convincing. Existing works usually model the users and products to make predictions, while the task studied in this paper falls into the detection category. According to Line 182, the task aims to predict the user's rating y given the current condition that contains the review document. As I understand, the review document is almost sufficient to determine the sentiment. And the BERT performance also acknowledges the importance of review documents. I don't think it is essential to build complex models to capture user and product dynamics in such a scenario.\n\nR2: The novelty of the model is limited. The main architecture comprises MUSE and JODIE with slight modifications.\n \nR2.1: It is unclear how MUSE can capture global and local information for users and products respectively. No corresponding results are provided.\n\nR2.2: The adaption of JODIE is questionable. In the original JODIE, the projection module is used for future prediction, while this paper takes it to update the representations of users and products. The design is unreasonable as I understand.\n\nR3: Though extensive experiments are reported, the results cannot provide sufficient useful information to demonstrate the superiority of the proposed model except for Section 4.2. Incorporating more tasks or diving deeper into the model's performance of capturing user and product dynamics may help. An illustration of the user/product embedding's trajectory can also help.", "questions_to_authors": "Q1: Is it necessary to build complex models to capture user and product dynamics to infer the sentiment of the given text? Does this task focus more on detection rather than prediction?\n\nQ2: Why is only the regression performance reported in Table 3?\n\nQ3: When dealing with online scenarios, can DC-DGNN deal with unseen users and products?", "paper_topic_and_main_contributions": "The main contribution of this paper falls into the \"NLP engineering experiment\" category. This paper aims to detect the sentiment of online streaming reviews. The authors claim that existing works neglect the time-varying nature of this scenario and propose DC-DGNN to capture user and product dynamics for sentiment analysis. DC-GNN designs a dual-channel dynamic graph updating module to model the incoming user-product review pairs and updates the representations of users and products accordingly. Experimental results demonstrate the effectiveness of the proposed model.", "excitement": 2, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "1. The meaning of p^{-}(t^{-}) in Line 291 is unclear.\n\n2. The notation is slightly different from those in the original JODIE paper. It is hard to understand what u(t^{-} + \\delta_{u}) means. \n\n3. It is unclear what \\delta_{u} means in Eq. (8). According to the JODIE paper, it should be p(t)-p(t^{-}).\n\n4. \\delta_{u} serves as a scalar in Eq. (8) while it denotes a vector in Eq. (9)."}}, {"review_id": "2OetgeK2uY", "reviewer": "Reviewer_axgv", "summary": "", "questions": "Please see \"reason to reject\".", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the comments. The major concerns of the reviewer are answered below.\n\n> My main concern is the lack of a temporal baseline. All the paper's baselines are non-temporal. I understand that there might not be a direct temporal study in sentiment analysis, but temporality has been well-studied in temporal recommender systems. At least 2-3 of such baselines, of course, with adaptation for the task of the paper, should be included.\n\nThe JODIE reported in the paper belongs to one of the baselines you mentioned. In addition, in the table below we supplement some baselines of Dynamic Graph Models in recommender systems adapted to this task. However, it is worth mentioning that their performance is actually in line with JODIE's performance, which is not good. In fact, these models also rely on text information to learn, but it is obvious that they do not really make full use of the text information and perform poorly on the task, which further reflects the significance of our work.\n\n- CAWN - Wang et al. ICLR2021\n  \n- DyGFormer - Yu et al. arxiv2023\n  \n- GraphMixer - Cong et al. ICLR2023\n  \n\n| Category | Model | accuracy | precision | recall | f1  | MSE | RMSE | MAE |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| Books | CAWN | 0.3144 | 0.0629 | 0.2 | 0.0957 | 0.9756 | 0.9877 | 0.7688 |\n|     | DyGFormer | 0.4785 | 0.0957 | 0.2 | 0.1295 | 1.5965 | 1.2635 | 0.8112 |\n|     | GraphMixer | 0.0201 | 0.004 | 0.2 | 0.0079 | 11.1066 | 3.3327 | 3.1888 |\n|     | **OURS** | **0.6814** |**0.6086**| **0.5363** | **0.5627** | **0.4044** | **0.6360** |**0.3449**|\n| Grocery_and_Gourmet_Food | CAWN | 0.208 | 0.0416 | 0.2 | 0.0689 | 1.0748 | 1.0367 | 0.8703 |\n|     | DyGFormer | 0.0302 | 0.006 | 0.2 | 0.0117 | 6.7165 | 2.5916 | 2.4585 |\n|     | GraphMixer | 0.024 | 0.0048 | 0.2 | 0.0094 | 12.5374 | 3.5408 | 3.4104 |\n|     | **OURS** | **0.7451** | **0.6207** | **0.5191** | **0.5021** | **0.4892** | **0.6994** | **0.3227** |\n| Kindle_Store | CAWN | 0.3606 | 0.0721 | 0.2 | 0.106 | 0.9216 | 0.96 | 0.7195 |\n|     | DyGFormer | 0.038 | 0.0076 | 0.2 | 0.0147 | 5.5645 | 2.3589 | 2.2027 |\n|     | GraphMixer | 0.021 | 0.0042 | 0.2 | 0.0082 | 10.8859 | 3.2994 | 3.1607 |\n|     | **OURS**| **0.6664** | **0.5912** | **0.5157** | **0.5370** | **0.3991** | **0.6318** | **0.3532** |\n| Sports_and_Outdoors | CAWN | 0.2056 | 0.0411 | 0.2 | 0.0682 | 1.0911 | 1.0446 | 0.8733 |\n|     | DyGFormer | 0.0189 | 0.0038 | 0.2 | 0.0074 | 7.1 | 2.6646 | 2.5622 |\n|     | GraphMixer | 0.03 | 0.006 | 0.2 | 0.0117 | 13.1044 | 3.62 | 3.5022 |\n|     | **OURS** | **0.7038** | **0.4227** | **0.3950** | **0.4063** | **0.5025** | **0.7089** | **0.3561** |\n| reviews_CDs_and_Vinyl | CAWN | 0.2491 | 0.0498 | 0.2 | 0.0798 | 1.1668 | 1.0802 | 0.8652 |\n|     | DyGFormer | 0.583 | 0.1166 | 0.2 | 0.1473 | 1.565 | 1.251 | 0.6991 |\n|     | GraphMixer | 0.0364 | 0.0073 | 0.2 | 0.0141 | 11.9721 | 3.4601 | 3.3009 |\n|     | **OURS**| **0.7243** | **0.5691** | **0.5435** | **0.5542** | **0.4084** | **0.6390** | **0.3134** |\n\n> The motivation for the temporal study is somehow not related to the current work. The authors claim that \"the users’ preferences and the popularity of products are continuously changing over time\"-L#055, which is true but not related. In review analysis, a user essentially writes a review about her experience of using the product. The popularity of a product or her preference may not have a direct influence on her review. At least this motivation needs more clarity by explaining it in Figure 1.\n\nAs we illustrate in the example, what the proposed method captures is the overall change of the user over time, including the user's interest and the user's language habits, which is directly related to sentiment analysis.\n\n> The experiment lacks information about the settings of the temporal study. While it includes hyperparameters of the model, it is missing what were the time intervals (yearly, I assume), which part was used for training (I assume streams of reviews before up until time t), the test part (I assume the t+1), etc ...\n\nFollowing the division method of the previous dynamic graph work, we first convert the time at the daily level into a timestamp, and then divide the data into a training set and a test set of 9:1 according to the timestamp. This method is adopted to ensure that during the training process, the model reads a relatively stable stream of data to avoid situations where there is too much or too little data. Also, the review stream before $t$ is used as the training set, and $t+1$ is used as the test set, but here $t$ represents the timespan divided according to the equal proportion of the timestamp, not a specific year or day.\n\n> No promise to release the codebase has been made for the reproducibility of the work.\n\nWe will release the code upon the acceptance of the paper.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "+ Well-structured and readable paper\n+ Sound formalization and evaluation methodology\n+ Adequate review categories for the training datasets", "reasons_to_reject": "- [resolved] ~~My main concern is the lack of a temporal baseline. All the paper's baselines are non-temporal. I understand that there might not be a direct temporal study in sentiment analysis, but temporality has been well-studied in temporal recommender systems. At least 2-3 of such baselines, of course, with adaptation for the task of the paper, should be included.~~\n\n- The motivation for the temporal study is somehow not related to the current work. The authors claim that \"the users’ preferences and the popularity of products are continuously changing over time\"-L#055, which is true but not related. In review analysis, a user essentially writes a review about her experience of using the product. The popularity of a product or her preference may not have a direct influence on her review. At least this motivation needs more clarity by explaining it in Figure 1.\n\n- [resolved if included in paper] ~~The experiment lacks information about the settings of the temporal study. While it includes hyperparameters of the model, it is missing what were the time intervals (yearly, I assume), which part was used for training (I assume streams of reviews before up until time t), the test part (I assume the t+1), etc ...~~\n\n- No promise to release the codebase has been made for the reproducibility of the work.", "questions_to_authors": "Please see \"reason to reject\".", "paper_topic_and_main_contributions": "The paper proposes to consume time information when performing sentiment analysis of reviews as users' preferences and products' aspects are changing over time. The proposed model adopts temporal gnn to incorporate temporality of users and products representation vectors as reviews are coming from the past to the current time interval in order to predict the sentiment of a user toward a product in the future yet-to-be-observed time interval.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "-- not sure I understood \"self-build\" dataset. They were all Amazon dataset of different product categories. Do you mean you order them in time or what?\n-- not sure I understood \"act as a current representation storage database\"?\n-- I would suggest use \"r\" for review and \"d\" for dimension :)\n-- there are few typos in terms of punctuations and the likes"}}]

Author rebuttal:

Meta-review: The reviewers are in general agreement that this paper could be accepted in EMNLP-2023. The reviewers also raised some issues with its presentation and motivation. The authors have provided a rebuttal that appeared to alleviate some concerns. A suggestion was made to accept the paper as the main conference or findings. The reviewers' concerns are also suggested to be addressed in the final version.
Decision: Accept-Main