Sentiment analysis has attracted increasing attention in e-commerce. The
sentiment polarities underlying user reviews are of great value for business
intelligence. Aspect category sentiment analysis (ACSA) and review rating
prediction (RP) are two essential tasks to detect the fine-to-coarse sentiment
polarities. %Considering the sentiment of the aspects(ACSA) and the overall
review rating(RP) simultaneously has the potential to improve the overall
performance. ACSA and RP are highly correlated and usually employed jointly in
real-world e-commerce scenarios. While most public datasets are constructed for
ACSA and RP separately, which may limit the further exploitation of both tasks.
To address the problem and advance related researches, we present a large-scale
Chinese restaurant review dataset \textbf{ASAP} including $46,730$ genuine
reviews from a leading online-to-offline (O2O) e-commerce platform in China.
Besides a $5$-star scale rating, each review is manually annotated according to
its sentiment polarities towards $18$ pre-defined aspect categories. We hope
the release of the dataset could shed some light on the fields of sentiment
analysis. Moreover, we propose an intuitive yet effective joint model for ACSA
and RP. Experimental results demonstrate that the joint model outperforms
state-of-the-art baselines on both tasks.

本文介绍了一项有关餐厅评论数据的研究，数据集包含了 46,730 条来自中国领先在线到线下（O2O）电子商务平台的餐厅评论，每条评论都按照其对 18 个预定义方面类别的情感极性进行手动注释，并提出了一种直观而有效的联合模型用于方面类别情感分析和评论评级预测，实验结果证明，该联合模型优于目前的基线模型。

ASAP: 面向方面分类情感分析和评级预测的中文评论数据集

ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment  Analysis and Rating Prediction

Review rating prediction of text reviews is a rapidly growing technology with
a wide range of applications in natural language processing. However, most
existing methods either use hand-crafted features or learn features using deep
learning with simple text corpus as input for review rating prediction,
ignoring the hierarchies among data. In this paper, we propose a Hierarchical
bi-directional self-attention Network framework (HabNet) for paper review
rating prediction and recommendation, which can serve as an effective
decision-making tool for the academic paper review process. Specifically, we
leverage the hierarchical structure of the paper reviews with three levels of
encoders: sentence encoder (level one), intra-review encoder (level two) and
inter-review encoder (level three). Each encoder first derives contextual
representation of each level, then generates a higher-level representation, and
after the learning process, we are able to identify useful predictors to make
the final acceptance decision, as well as to help discover the inconsistency
between numerical review ratings and text sentiment conveyed by reviewers.
Furthermore, we introduce two new metrics to evaluate models in data imbalance
situations. Extensive experiments on a publicly available dataset (PeerRead)
and our own collected dataset (OpenReview) demonstrate the superiority of the
proposed approach compared with state-of-the-art methods.

本文提出了一个基于层次自注意力网络框架（HabNet）的论文审阅评级预测和推荐方法，该方法能够作为学术论文审阅过程的有效决策工具，并通过三级编码器中的分层结构来识别有用的预测器，以及帮助发现评审者所传达的数值评级和文本情感之间的不一致性，进一步引入了两个新的度量标准来评估数据不平衡情况下的模型，经过对公开数据集（PeerRead）和我们自己收集的数据集（OpenReview）的广泛实验，证明了该方法相比于最先进的方法有着更为卓越的表现。

用于论文评审评估推荐的分层双向自注意力网络

Hierarchical Bi-Directional Self-Attention Networks for Paper Review  Rating Recommendation

Review websites, such as TripAdvisor and Yelp, allow users to post online
reviews for various businesses, products and services, and have been recently
shown to have a significant influence on consumer shopping behaviour. An online
review typically consists of free-form text and a star rating out of 5. The
problem of predicting a user's star rating for a product, given the user's text
review for that product, is called Review Rating Prediction and has lately
become a popular, albeit hard, problem in machine learning. In this paper, we
treat Review Rating Prediction as a multi-class classification problem, and
build sixteen different prediction models by combining four feature extraction
methods, (i) unigrams, (ii) bigrams, (iii) trigrams and (iv) Latent Semantic
Indexing, with four machine learning algorithms, (i) logistic regression, (ii)
Naive Bayes classification, (iii) perceptrons, and (iv) linear Support Vector
Classification. We analyse the performance of each of these sixteen models to
come up with the best model for predicting the ratings from reviews. We use the
dataset provided by Yelp for training and testing the models.

本文使用 16 种不同的特征提取方法（包括 unigrams、bigrams、trigrams 和潜在语义索引）和 4 种机器学习算法（包括逻辑回归、朴素贝叶斯分类、感知器和线性支持向量分类）将评论评级预测视为多类分类问题，并分析了每种模型的性能，从而提出了最佳模型。使用 Yelp 数据集来训练和测试模型。