Review Text(审查文本)研究综述
Review Text 审查文本 - , the sentiment of review texts) and that reviewers tend to be less critical for lower priced products. [1] It extracted products' attributes from review text using Bigram analysis and measured the number of attributes discussed in a review. [2] We applied a word-level bigram analysis to derive product attributes from review text and examined the influence of the number of attributes on the review's helpfulness votes. [3] A hierarchical attention network is applied to fully extract the information in the review text, which emphasizes the important keywords and phrases. [4] We find that it is not only the mere presence of a photo that increases helpfulness but also the similarity between the photo content and the review text. [5] Review text is a valuable source of information for recommendation systems and often contains rich semantics with user preferences and item attributes. [6] The proposed model FP2GN identifies the aspect terms in review text using sentic computing (SenticNet 5 and concept frequency-inverse opinion frequency) and statistical feature engineering. [7] To model such customer expectations and capture important information from a review text, we propose a novel neural network which leverages review sentiment and product information. [8] We present a novel technique using aspect markers that learns to generate personalized explanations of recommendations from review texts, and we show that human users significantly prefer these explanations over those produced by state-of-the-art techniques. [9] Finally, a sigmoid activation function as the last layer of the proposed model receives the input sequences from the previous layer and performs binary classification task of review text into fake or truthful. [10] analyzing more found questions (2) HOTS questions among types of text, news texts and persuasion texts are the same number of questions found, slogan and poster ad text, exposition text, fiction and non fiction text found, explanatory texts and review texts are the same number of questions found, drama texts and poetry texts found the most questions. [11] This method takes into account both semantic indicators (emotional factors and ontological features) and statistical indicators (review length), considers comprehensive information in the review text and has better domain adaptability. [12] To alleviate the sparsity issue, many recommender systems have been proposed to consider the review text as the auxiliary information to improve the recommendation quality. [13] Results show that images affect the relationship between review text and purchase intention as well as trust for both product categories. [14] This study aims to generate interactive word cloud—Cirrus—on the basis of statistical data to preview text of the novel for readers. [15] According to the latest studies in this field, using review texts could not only improve the performance of recommendation, but it can also alleviate the impact of data sparsity and help to tackle the cold start problem. [16] Our approach considers both the rating score as well as the review text through a probabilistic topic modeling method, providing also a roadmap to quantify and exploit employee big data analytics. [17] Review text and reviewer behavior are factors considered to detect spam opinions. [18] Discussion this was done in review texts and the results of research having relevance for the purpose subjects, writer take some forms of the development of culture and an effect on cultural took. [19] Since most commercial website nowadays, allows user to express their opinion through the review text, then there is an opportunity to precisely understand the user preferences via this element. [20] The purpose of this paper is to design a structure for analyzing the text to quantify the consumer satisfaction hidden behind the review text, so as to guide sellers and consumers to a more refined understanding of the potential consumption behavior and make the comparison easier and more direct. [21] Unsupervised deep aspect-level sentiment model employing deep Boltzmann machines first learns fine-grained opinion representations from review texts. [22] In this study, we proposed a model to transform the rating scores of grumpy users to match with other users by using users’ review text, then we used those ratings for improving the performance of the recommender systems. [23] However, such approaches are limited in that they: a) do not explore the usage of both the reviewer and area chair recommendations, b) do not explicitly model subjectivity on a per submission basis, and c) are not applicable in realistic settings, by assuming that review texts are available at test time, when these are exactly the inputs that should be considered to be missing in this application. [24] This study investigated the effects of a Genre-Based Approach (gba) on 54 participants’ abilities to write a review text of a mobile application or website while reflecting on the “evaluating a text” function embedded in the target genre. [25] 8%), review texts (100%), persuasive texts (95. [26] We use an innovative model, Bi-LSTM model to calculate the rate of different emotions contained in the review texts. [27] Furthermore, most of the existing recommenders studied on temporal dynamics hidden in user-item interactions by using ratings or review texts solely, without utilizing these heterogeneous side information in a comprehensive manner. [28] Aspect based sentiment analysis (ABSA) is a valuable task, aiming to predict the sentiment polarities of the given aspects (terms or categories) in review texts. [29] This research is examined through qualitative approach combining observation and review texts. [30] To solve this problem, we use the review text and its specific aspect information to construct a multi-level, high-dimensional deep neural network model. [31] However, it is appropriate for this work’s limited scope as a review text (as disclosed by the authors’ preface) and suitable to identify learning gaps motivating the reader toward further study. [32] The review texts include “condemnation letters with condolence letters written in the context of assault, accident, death”. [33] A sentiment analysis was conducted to quantify the perceptions of the consumer nutrition environment in the review text. [34] The focus of this study is to find answers to the level of students 'abilities in the affixation process with the aim of describing the percentage of students' ability to write the review text. [35] Most existing hybrid CF methods try to incorporate side information such as review texts to alleviate the data sparsity problem. [36] We use a Joint Sentiment-Topic model to extract the topics and associated sentiments in review texts. [37] Kata Kunci: analisis wacana, resensi, teks ulasan Abstrack: This research aimed at describing and explaining about (1) the discourse structure of review text on book review columns in Solopos Newspaper January-December 2017 edition; (2) the textual aspects of book review; (3) the contextual aspects of book review; (4) the relevance of book review columns in Solopos Newspaper January-December 2017 edition as the teaching materials of review text in Secondary Junior High Schools and in Secondary Senior High Schools. [38] Four variables L (text length), T (period time), P (with or without a picture) and S (sentiment intensity) are derived to measure review helpfulness from review text. [39] This paper mainly studies the personalized rating prediction task based on review texts for the recommendation. [40] Using this information from users’ location history, we predict user ratings by harnessing the information present in review text as well as consider social influence from similar user set formed based on matching category preferences and similar reviews. [41] Further analyses of the review texts show that Western and Japanese consumers express their sentiments over different dimensions of restaurant experience (food quality, service quality, the physical environment, and price fairness) for the same categories of Japanese dish. [42] Previous work studied key determinant factors of review helpfulness, such as product metadata and review text. [43] This research aimed at investigating the ability of the fourth semester English Education Department students at the University of Potensi Utama, Medan, in writing a review text of a novel entitled ‘Sengsara Membawa Nikmat’ written by Toelis Soetan Sati. [44] Opinion mining, the subfield of text mining, deals with mining of review text and classifying the opinions or the sentiments of that text as positive or negative. [45] Our model incorporated both semantic relationship of review text and product information. [46] Thirdly, it facilitates to justify the rating with review text. [47] In this paper, we propose a collaborative filtering system based on attention mechanism and design the feature-topic model to extract the characteristics of the item from review texts. [48] This paper presents the DIversifying Personalized Mobile Multimedia Application Recommendation (DIPMMAR) by fusing the user ratings, review texts, application description, and application popularity. [49] , images and review texts, and the patterns in the rating matrix itself is rarely touched. [50]，评论文本的情绪），并且评论者往往对低价产品不太挑剔。 [1] 它使用 Bigram 分析从评论文本中提取产品的属性，并测量评论中讨论的属性数量。 [2] 我们应用了词级二元分析从评论文本中获取产品属性，并检查了属性数量对评论的有用性投票的影响。 [3] 应用分层注意力网络来充分提取评论文本中的信息，强调重要的关键词和短语。 [4] 我们发现，不仅照片的存在会增加帮助性，而且照片内容和评论文本之间的相似性也会增加。 [5] 评论文本是推荐系统的宝贵信息来源，通常包含具有用户偏好和项目属性的丰富语义。 [6] 所提出的模型 FP2GN 使用情感计算（SenticNet 5 和概念频率-反向意见频率）和统计特征工程识别评论文本中的方面术语。 [7] 为了对此类客户期望进行建模并从评论文本中捕获重要信息，我们提出了一种利用评论情绪和产品信息的新型神经网络。 [8] 我们提出了一种使用方面标记的新技术，该技术学习从评论文本中生成个性化的推荐解释，并且我们表明人类用户更喜欢这些解释而不是最先进的技术产生的解释。 [9] 最后，作为所提出模型的最后一层的 sigmoid 激活函数接收来自前一层的输入序列，并执行将评论文本分为虚假或真实的二元分类任务。 [10] 分析更多发现的问题（2）文本类型之间的热点问题，新闻文本和说服文本发现的问题数量相同，标语和海报广告文本，说明文本，小说和非小说文本，解释性文本和评论文本是发现的问题数量相同，戏剧文本和诗歌文本发现的问题最多。 [11] 该方法同时考虑了语义指标（情感因素和本体特征）和统计指标（评论长度），考虑了评论文本中的综合信息，具有更好的领域适应性。 [12] 为了缓解稀疏问题，许多推荐系统被提出将评论文本作为辅助信息来提高推荐质量。 [13] 结果表明，图像会影响评论文本与购买意愿之间的关系以及对这两个产品类别的信任。 [14] 本研究旨在基于统计数据生成交互式词云——Cirrus，以供读者预览小说文本。 [15] 根据该领域的最新研究，使用评论文本不仅可以提高推荐的性能，还可以减轻数据稀疏的影响，有助于解决冷启动问题。 [16] 我们的方法通过概率主题建模方法同时考虑评分和评论文本，还提供了量化和利用员工大数据分析的路线图。 [17] 评论文本和评论者行为是检测垃圾评论的考虑因素。 [18] 讨论这是在评论文本中进行的，研究结果与目的主题相关，作者采取了某种形式的文化发展和对文化的影响。 [19] 由于现在大多数商业网站都允许用户通过评论文本来表达自己的意见，那么就有机会通过这个元素准确地了解用户的偏好。 [20] 本文的目的是设计一种文本分析结构，量化隐藏在评论文本背后的消费者满意度，从而引导卖家和消费者对潜在的消费行为有更精细的理解，让对比变得更容易、更直接。 . [21] 使用深度玻尔兹曼机的无监督深度方面级情感模型首先从评论文本中学习细粒度的意见表示。 [22] 在这项研究中，我们提出了一个模型，通过使用用户的评论文本将脾气暴躁的用户的评分转换为与其他用户匹配，然后我们使用这些评分来提高推荐系统的性能。 [23] 然而，这些方法的局限性在于：a) 不探索审稿人和区域主席建议的使用，b) 不明确对每个提交的主观性建模，c) 不适用于现实环境，通过假设审查文本在测试时可用，而这些正是本应用程序中应被视为缺失的输入。 [24] 本研究调查了基于类型的方法 (gba) 对 54 名参与者编写移动应用程序或网站的评论文本的能力的影响，同时反映了嵌入目标类型中的“评估文本”功能。 [25] 8%），评论文本（100%），有说服力的文本（95. [26] 我们使用创新模型 Bi-LSTM 模型来计算评论文本中包含的不同情绪的比率。 [27] 此外，大多数现有的推荐器仅通过使用评分或评论文本来研究隐藏在用户-项目交互中的时间动态，而没有全面利用这些异构的辅助信息。 [28] 基于方面的情感分析（ABSA）是一项有价值的任务，旨在预测评论文本中给定方面（术语或类别）的情感极性。 [29] 这项研究是通过结合观察和评论文本的定性方法来检验的。 [30] 为了解决这个问题，我们使用评论文本及其特定方面的信息来构建一个多层次、高维的深度神经网络模型。 [31] 然而，它适合作为评论文本（如作者前言所披露）的这项工作的有限范围，并且适合识别激励读者进一步学习的学习差距。 [32] 审查文本包括“在袭击、事故、死亡的情况下写的谴责信和吊唁信”。 [33] 进行情绪分析以量化评论文本中消费者营养环境的看法。 [34] 本研究的重点是在词缀过程中寻找学生能力水平的答案，目的是描述学生撰写评论文本的能力百分比。 [35] 大多数现有的混合 CF 方法都试图结合诸如评论文本之类的辅助信息来缓解数据稀疏问题。 [36] 我们使用联合情感主题模型来提取评论文本中的主题和相关情感。 [37] Kata Kunci: analisis wacana, resensi, teks ulasan 摘要：本研究旨在描述和解释（1）Solopos Newspaper 2017年1-12月版书评栏目评论文本的语篇结构； （2）书评的文本方面； (3) 书评的语境方面； (4) Solopos Newspaper 2017年1-12月版书评栏目作为初中和高中复习课文教材的相关性。 [38] 推导出四个变量 L（文本长度）、T（周期时间）、P（有或没有图片）和 S（情感强度）来衡量来自评论文本的评论有用性。 [39] 本文主要研究基于评论文本的个性化评分预测任务进行推荐。 [40] 使用来自用户位置历史的这些信息，我们通过利用评论文本中存在的信息来预测用户评分，并考虑来自基于匹配类别偏好和类似评论形成的类似用户集的社会影响。 [41] 对评论文本的进一步分析表明，西方和日本消费者对同一类别的日本菜的餐厅体验（食品质量、服务质量、物理环境和价格公平）的不同维度表达了他们的情感。 [42] 以前的工作研究了评论有用性的关键决定因素，例如产品元数据和评论文本。 [43] 本研究旨在调查棉兰 Potensi Utama 大学第四学期英语教育系学生对 Toelis Soetan Sati 所著小说《Sengsara Membawa Nikmat》的评论文本的写作能力。 [44] 意见挖掘是文本挖掘的子领域，处理评论文本的挖掘并将该文本的意见或情绪分类为正面或负面。 [45] 我们的模型结合了评论文本和产品信息的语义关系。 [46] 第三，它有助于用评论文本来证明评级的合理性。 [47] 在本文中，我们提出了一种基于注意力机制的协同过滤系统，并设计了特征主题模型来从评论文本中提取项目的特征。 [48] 本文通过融合用户评分、评论文本、应用描述和应用流行度，提出了多样化的个性化移动多媒体应用推荐（DIPMMAR）。 [49] ，图像和评论文本，评分矩阵本身的模式很少被触及。 [50]
Product Review Text 产品评论文本
First, use the BERT model to obtain the feature representation of the product review text, and then input the obtained feature representation into the BiLSTM network to extract the emotional features of the product review. [1] We elicited domain knowledge from a product review text corpus and integrated the knowledge into a bidirectional long short-term memory-based multitask learning network. [2] Many of the current SA techniques for these customer online product review text data have low accuracy and often takes longer time in the course of training. [3] Store the product sales specification text and product review text as divergent texts for the next stage of data cleaning to predict user consumption behavior. [4] We classified the beauty product review texts as spam and non-spam reviews. [5] Our empirical evaluation on two tasks – formality classification and sarcasm detection – shows that the cross-cultural difference between German and American English, as manifested in product review text, can be applied to achieve good performance for formality classification, while the difference between Japanese and American English can be applied to achieve good performance for sarcasm detection – both without any task-specific labeled data. [6] In order to overcome the shortcomings of existing sentiment analysis models, this paper puts forward a sentiment analysis model based on stacked bidirectional Gated Recurrent Unit (Stacked Bi-GRU) with a convolutional neural network (CNN) and attention mechanism (SBCAM) to solve sentiment analysis problem of Chinese product review text. [7]首先，使用BERT模型获取产品评论文本的特征表示，然后将获得的特征表示输入BiLSTM网络，提取产品评论的情感特征。 [1] 我们从产品评论文本语料库中提取领域知识，并将这些知识整合到基于双向长短期记忆的多任务学习网络中。 [2] 目前针对这些客户在线产品评论文本数据的许多 SA 技术准确性较低，并且通常在训练过程中需要较长时间。 [3] nan [4] nan [5] 我们对形式分类和讽刺检测这两个任务的实证评估表明，德国和美国英语之间的跨文化差异，如产品评论文本中所表现的那样，可以用于形式分类取得良好的性能，而日语和美国英语之间的差异可以用于形式分类。可以应用美式英语来实现良好的讽刺检测性能——两者都不需要任何特定于任务的标记数据。 [6] 为了克服现有情感分析模型的不足，本文提出了一种基于堆叠双向门控循环单元（Stacked Bi-GRU）的情感分析模型，结合卷积神经网络（CNN）和注意力机制（SBCAM）来解决情感问题。中文产品评论文本的分析问题。 [7]
Peer Review Text
We then analyze student projects and peer review text via sentiment analysis to infer insights for visualization educators, including the focus of course content, engagement across student groups, student mastery of concepts, course trends over time, and expert intervention effectiveness. [1] An important kind of data signals, peer review text, has not been utilized for the CCP task. [2] This paper extends the existing research on the anonymous peer review and aims to deepen our understanding of this genre by analysing the overall functional organization of peer review texts and their prominent linguistic features shaped by three communicative functions ‒ “gatekeeping”, evaluative, and didactic. [3] Peer review texts reflect the overall impression of the reviewers towards a candidate research paper and by far are the most important? artifact used by editors and program chairs to determine the prospective inclusion of a manuscript in a given journal or a conference. [4] However, the peer review texts, which contains rich sentiment information of the reviewer, reflecting his/her overall attitude towards the research in the paper, could be a valuable entity to predict the acceptance or rejection of the manuscript under consideration. [5]然后，我们通过情感分析分析学生项目和同行评议文本，以推断可视化教育工作者的见解，包括课程内容的重点、跨学生群体的参与、学生对概念的掌握、随着时间的推移的课程趋势以及专家干预的有效性。 [1] 一种重要的数据信号，同行评审文本，尚未用于 CCP 任务。 [2] 本文扩展了对匿名同行评议的现有研究，旨在通过分析同行评议文本的整体功能组织及其由“把关”、评价和教学三个交际功能塑造的突出语言特征来加深我们对这一类型的理解。 [3] 同行评审文本反映了审稿人对候选研究论文的总体印象，到目前为止是最重要的吗？编辑和项目主席使用的人工制品，用于确定是否将手稿纳入给定期刊或会议。 [4] nan [5]
Online Review Text 在线评论文本
The results show that the length of MOOC online review text is affected by the MOOC learning progress, the number of discussion forum posts, the number of follow, the online review sentiment and MOOC rating. [1] This study developed a text mining method to quantify constructs using a large-scale sample of 3,500,445 online review texts. [2] The sample included 572 wineries from all 13 German wine regions with website text data and online review text data from each winery. [3] The purpose of this paper is to aggregate the topic information of online review text and clarify the user needs. [4]结果表明，MOOC在线评论文本的长度受MOOC学习进度、论坛发帖数、关注数、在线评论情绪和MOOC评分的影响。 [1] 本研究开发了一种文本挖掘方法，使用 3,500,445 个在线评论文本的大规模样本来量化结构。 [2] 样本包括来自所有 13 个德国葡萄酒产区的 572 家酒厂，以及每个酒厂的网站文本数据和在线评论文本数据。 [3] 本文的目的是聚合在线评论文本的主题信息，明确用户需求。 [4]
Write Review Text 撰写评论文本
Learning To Write Review Text In Class XI SMA Negeri 11 Pangkep Universitas Negeri Makassar. [1] This study describes online learning to write review texts using themethod Student Teams Achievement Divisions. [2] (3) There is no interaction between the learning model and the ability to think critically about the ability to write review texts. [3] This research is motivated by the curiosity of researchers regarding the ability of students to write review texts from the results of the pretest and posttest using the problem based learning model. [4]在 XI SMA Negeri 11 Pangkep Makassar 州立大学学习写评论文本。 [1] 本研究描述了使用学生团队成就部门的方法在线学习撰写评论文本。 [2] (3) 学习模式与批判性思考能力和撰写评论文本的能力之间没有相互作用。 [3] 这项研究的动机是研究人员对学生使用基于问题的学习模型根据前测和后测结果撰写评论文本的能力的好奇心。 [4]
Film Review Text
The sentiment analysis of the film review text is to extract and analyze the hidden sentiment information in the text data, thereby helping the network personnel such as the media platform to analyze the audience's preference for the film. [1] This study aims to produce interactive multimedia development in learning of film review text for 8 th grade students in Senior High School (SMP) 1 Tanjungmorawa. [2]影评文本情感分析是对文本数据中隐藏的情感信息进行提取和分析，从而帮助媒体平台等网络人员分析观众对电影的偏好。 [1] 本研究旨在为丹戎莫拉瓦高中 (SMP) 1 的 8 年级学生在电影评论文本学习中提供交互式多媒体开发。 [2]
Movie Review Text
The description text of the film will be classified into 10 classes with the number of training data as many as 1028, while the movie review text will be classified into 5 classes with the number of training data as many as 10032. [1] The authors also explore usage of convolution and max-pooling neural layers on song lyrics, product and movie review text data sets. [2]电影的描述文本将分为 10 类，训练数据数多达 1028 个，而电影评论文本将分为 5 个类，训练数据数多达 10032 个。 [1] 作者还探索了在歌词、产品和电影评论文本数据集上使用卷积和最大池化神经层。 [2]
Systematic Review Text
Although it is a critical process, few guidelines have been put forth since the publications of seminal systematic review textbooks. [1] Interested readers may refer to systematic review texts to conduct a thorough meta-analysis. [2]尽管这是一个关键过程，但自从开创性的系统评价教科书出版以来，几乎没有提出任何指导方针。 [1] 有兴趣的读者可以参考系统综述文本进行全面的荟萃分析。 [2]
review text datum 审查文本基准
Many of the current SA techniques for these customer online product review text data have low accuracy and often takes longer time in the course of training. [1] The sample included 572 wineries from all 13 German wine regions with website text data and online review text data from each winery. [2] The dataset is a clothing review text data taken from Kaggle. [3] The authors also explore usage of convolution and max-pooling neural layers on song lyrics, product and movie review text data sets. [4] This analysis produces knowledge about sentiment from the review text data using approaches of n-grams to increase the level of accuracy according to the literature proven. [5]目前针对这些客户在线产品评论文本数据的许多 SA 技术准确性较低，并且通常在训练过程中需要较长时间。 [1] 样本包括来自所有 13 个德国葡萄酒产区的 572 家酒厂，以及每个酒厂的网站文本数据和在线评论文本数据。 [2] 该数据集是取自 Kaggle 的服装评论文本数据。 [3] 作者还探索了在歌词、产品和电影评论文本数据集上使用卷积和最大池化神经层。 [4] nan [5]
review text feature 查看文本功能
In all this work, either review text features or review metadata features to identify the review. [1] CF-based approach usually resorts to matrix factorization based on user–item interaction, and does not fully utilize the valuable review text features. [2] Most of the existing research on opinion spam detection uses the traditional bag-of-words model to represent the review text features and apply standard machine learning models such as Support-Vector Machines or Naïve Bayes as classifiers. [3]在所有这些工作中，要么审查文本特征，要么审查元数据特征来识别评论。 [1] 基于CF的方法通常采用基于用户-项目交互的矩阵分解，并没有充分利用有价值的评论文本特征。 [2] nan [3]