Toxic contents in online product review are a common phenomenon. A content is
perceived to be toxic when it is rude, disrespectful, or unreasonable and make
individuals leave the discussion. Machine learning algorithms helps the sell
side community to identify such toxic patterns and eventually moderate such
inputs. Yet, the extant literature provides fewer information about the
sentiment of a prospective consumer on the perception of a product after being
exposed to such toxic review content. In this study, we collect a balanced data
set of review comments from 18 different players segregated into three
different sectors from google play-store. Then we calculate the sentence-level
sentiment and toxicity score of individual review content. Finally, we use
structural equation modelling to quantitatively study the influence of toxic
content on overall product sentiment. We observe that comment toxicity
negatively influences overall product sentiment but do not exhibit a mediating
effect over reviewer score to influence sector-wise relative rating.

本研究收集了来自 Google Play 商店的产品评论数据，运用机器学习算法进行情感分析和毒性评分，结构方程建模量化研究发现毒性评论对产品整体情感有负面影响，但不会通过评论者评分对不同领域相对评分产生调节作用。

有害评论内容对产品总体情感的影响

Effect of Toxic Review Content on Overall Product Sentiment

Opinion summarization is the automatic creation of text reflecting subjective
information expressed in multiple documents, such as user reviews of a product.
The task is practically important and has attracted a lot of attention.
However, due to the high cost of summary production, datasets large enough for
training supervised models are lacking. Instead, the task has been
traditionally approached with extractive methods that learn to select text
fragments in an unsupervised or weakly-supervised way. Recently, it has been
shown that abstractive summaries, potentially more fluent and better at
reflecting conflicting information, can also be produced in an unsupervised
fashion. However, these models, not being exposed to actual summaries, fail to
capture their essential properties. In this work, we show that even a handful
of summaries is sufficient to bootstrap generation of the summary text with all
expected properties, such as writing style, informativeness, fluency, and
sentiment preservation. We start by training a conditional Transformer language
model to generate a new product review given other available reviews of the
product. The model is also conditioned on review properties that are directly
related to summaries; the properties are derived from reviews with no manual
effort. In the second stage, we fine-tune a plug-in module that learns to
predict property values on a handful of summaries. This lets us switch the
generator to the summarization mode. We show on Amazon and Yelp datasets that
our approach substantially outperforms previous extractive and abstractive
methods in automatic and human evaluation.

本文介绍了一种学习无监督条款，在生成模型中引入少量样本来捕获其必要属性的方法，用于生成商品评论等主观性文本的摘要，该方法比以往的提取和抽象方法更有效。

观点摘要的小样本学习

Few-Shot Learning for Opinion Summarization

We present a model for aggregation of product review snippets by joint aspect
identification and sentiment analysis. Our model simultaneously identifies an
underlying set of ratable aspects presented in the reviews of a product (e.g.,
sushi and miso for a Japanese restaurant) and determines the corresponding
sentiment of each aspect. This approach directly enables discovery of
highly-rated or inconsistent aspects of a product. Our generative model admits
an efficient variational mean-field inference algorithm. It is also easily
extensible, and we describe several modifications and their effects on model
structure and inference. We test our model on two tasks, joint aspect
identification and sentiment analysis on a set of Yelp reviews and aspect
identification alone on a set of medical summaries. We evaluate the performance
of the model on aspect identification, sentiment analysis, and per-word
labeling accuracy. We demonstrate that our model outperforms applicable
baselines by a considerable margin, yielding up to 32% relative error reduction
on aspect identification and up to 20% relative error reduction on sentiment
analysis.

我们提出了一个模型，通过联合方面识别和情感分析来汇总产品评论片段。我们的模型同时确定了评论中呈现的可评估方面的基础集，并确定了每个方面的相应情感，从而直接发现了产品的高评级或不一致方面。

通过联合建模方面和价值自动聚合

Automatic Aggregation by Joint Modeling of Aspects and Values

Product review nowadays has become an important source of information, not
only for customers to find opinions about products easily and share their
reviews with peers, but also for product manufacturers to get feedback on their
products. As the number of product reviews grows, it becomes difficult for
users to search and utilize these resources in an efficient way. In this work,
we build a product review summarization system that can automatically process a
large collection of reviews and aggregate them to generate a concise summary.
More importantly, the drawback of existing product summarization systems is
that they cannot provide the underlying reasons to justify users' opinions. In
our method, we solve this problem by applying clustering, prior to selecting
representative candidates for summarization.

本文介绍了一种可以自动处理大量评论并汇总生成简明摘要的产品评论汇总系统，通过应用聚类并选择代表性候选项来解决现有产品汇总系统无法提供支持用户意见的根本原因的缺点。