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Review Text sentence examples within Product Review Text
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.
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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.
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Review Text sentence examples within 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.
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An important kind of data signals, peer review text, has not been utilized for the CCP task.
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Review Text sentence examples within Online Review Text
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.
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This study developed a text mining method to quantify constructs using a large-scale sample of 3,500,445 online review texts.
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Review Text sentence examples within Write Review Text
Review Text sentence examples within 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.
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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.
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Review Text sentence examples within 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.
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The authors also explore usage of convolution and max-pooling neural layers on song lyrics, product and movie review text data sets.
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Review Text sentence examples within Systematic Review Text
Review Text sentence examples within review text datum
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.
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The sample included 572 wineries from all 13 German wine regions with website text data and online review text data from each winery.
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Review Text sentence examples within review text feature
, the sentiment of review texts) and that reviewers tend to be less critical for lower priced products.
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It extracted products' attributes from review text using Bigram analysis and measured the number of attributes discussed in a review.
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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.
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A hierarchical attention network is applied to fully extract the information in the review text, which emphasizes the important keywords and phrases.
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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.
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Review text is a valuable source of information for recommendation systems and often contains rich semantics with user preferences and item attributes.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Results show that images affect the relationship between review text and purchase intention as well as trust for both product categories.
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This study aims to generate interactive word cloud—Cirrus—on the basis of statistical data to preview text of the novel for readers.
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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.
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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.
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Review text and reviewer behavior are factors considered to detect spam opinions.
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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.
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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.
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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.
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Unsupervised deep aspect-level sentiment model employing deep Boltzmann machines first learns fine-grained opinion representations from review texts.
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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.
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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.
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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.
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8%), review texts (100%), persuasive texts (95.
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We use an innovative model, Bi-LSTM model to calculate the rate of different emotions contained in the review texts.
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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.
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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.
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This research is examined through qualitative approach combining observation and review texts.
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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.
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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.
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The review texts include “condemnation letters with condolence letters written in the context of assault, accident, death”.
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A sentiment analysis was conducted to quantify the perceptions of the consumer nutrition environment in the review text.
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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.
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Most existing hybrid CF methods try to incorporate side information such as review texts to alleviate the data sparsity problem.
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We use a Joint Sentiment-Topic model to extract the topics and associated sentiments in review texts.
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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.
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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.
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This paper mainly studies the personalized rating prediction task based on review texts for the recommendation.
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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.
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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.
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Previous work studied key determinant factors of review helpfulness, such as product metadata and review text.
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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.
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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.
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Our model incorporated both semantic relationship of review text and product information.
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Thirdly, it facilitates to justify the rating with review text.
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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.
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This paper presents the DIversifying Personalized Mobile Multimedia Application Recommendation (DIPMMAR) by fusing the user ratings, review texts, application description, and application popularity.
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, images and review texts, and the patterns in the rating matrix itself is rarely touched.
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The results from the quantitative experimental design study (Study 3) find that language style and emotions influence customer perceptions of poster, website and firm trustworthiness, which also mediates the relationship between the qualitative aspects of review text on behavioral intentions.
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, product images, descriptions and review texts) into account and design an end-to-end recommendation model.
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