Introduction to Review Text

, the sentiment of review texts) and that reviewers tend to be less critical for lower priced products.

In all this work, either review text features or review metadata features to identify the review.

It extracted products' attributes from review text using Bigram analysis and measured the number of attributes discussed in a review.

From the role of review text clustering analysis, this paper derives two dimensions from e-commerce platforms and skin care product categories, and through feature extraction and lexical item clustering analysis of consumer online review information on different platforms, the focus of attention and characteristic tendencies of consumers on skin care products on different platforms are mined.

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.

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.

A hierarchical attention network is applied to fully extract the information in the review text, which emphasizes the important keywords and phrases.

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.

For attaining a stable syntactic pattern set, propagation and refinement process is done and the final review text is considered to be an opinion of specific product.

Review text is a valuable source of information for recommendation systems and often contains rich semantics with user preferences and item attributes.

The dataset contains review text written not only in standard and colloquial Indonesian languages but also standard and colloquial English, labeled by BIO format notation.

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.

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.

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.

A transformer model is used to generate individual sentence summaries of respected review text and then used a combination of Universal Sentence Encoder, statistical methods and graph reduction algorithm to select the most relevant sentences to best represent the whole text.

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.

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.

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.

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.

Results show that images affect the relationship between review text and purchase intention as well as trust for both product categories.

This study aims to generate interactive word cloud—Cirrus—on the basis of statistical data to preview text of the novel for readers.

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.

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.

Learning To Write Review Text In Class XI SMA Negeri 11 Pangkep Universitas Negeri Makassar.

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.

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.

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.

This study describes online learning to write review texts using themethod Student Teams Achievement Divisions.

Review text and reviewer behavior are factors considered to detect spam opinions.

CF-based approach usually resorts to matrix factorization based on user–item interaction, and does not fully utilize the valuable review text features.

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.

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.

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.

Besides, this study selects a biterm topic model that is suitable to deal with short review texts to mine the main attributes.

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.

The review text received by crowdsourcing participants contains valuable knowledge, opinions, and preferences, which is an important basis for employers to make trading decisions, and crowdsourcing participants to improve service level and quality.

Unsupervised deep aspect-level sentiment model employing deep Boltzmann machines first learns fine-grained opinion representations from review texts.

The result is a model that can accurately identify descriptors within a corpus of whisky review texts with a train/test accuracy of 99% and precision, recall, and F1-scores of 0.

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.

IHFSSA first identifies sentiment features in the review text utilizing Penn Treebank part-of-speech tagset and integrated Wide Coverage Sentiment Lexicons (WCSL).

This paper will propose an effective multi-dimension attention convolutional neural networks (MACNNs) model to analyze customer review texts and predict the pension service quality.

Lavrovskij, Potebnja’s  teacher, who wrote and published (1866) a voluminous critical review text (102 pages) in the genre of razbor  „an analytical book review” which represented the reviewing traditions of the academic discourse in XIX c.

This study developed a text mining method to quantify constructs using a large-scale sample of 3,500,445 online review texts.

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.

Feature design and recognition method design are the key steps for false review text recognition.

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.

8%), review texts (100%), persuasive texts (95.

On the basis of an experimental research performed through 2500 review texts as dataset, the best performance was obtained that had accuracy of 85.

We use an innovative model, Bi-LSTM model to calculate the rate of different emotions contained in the review texts.

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.

This study proposed the robust defective products’ identification framework and examined un-investigated review textual features.

In this article, we propose a needs-based configurator mechanism that leverages online product-review text from social media.

In this paper, we propose an attention-based multilevel interactive neural network model with aspect constraints that mines the multilevel implicit expression mode of reviews and integrates four dimensions, namely, users, review texts, products and fine-grained aspects, into review representations.

Online product review data from Amazon, one of the leading online shopping websites globally, and Rakuten, one of the representative online shopping websites in Japan, were used to reveal the hidden topics in the review texts.

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.

As a natural language generation task, it is challenging to generate informative and coherent review text.

Furthermore, only 14% (5/37) of reviews mentioned patient or public involvement as authors in the abstract; involvement was often only indicated in the author affiliation field or in the review text (most often in the methods or contributions section).

Therefore, combining a user-item interaction graph with related review text will obtain better recommendation performance.

The film / drama’s review texts discussed in the 2013 curriculum require the students be able to produce the review texts which suitable with their language structure and rules.

Many existing attention-based deep learning approaches to sentiment analysis have focused on words and represent an entire review text as a word sequence.

Based on the individual level of consumers, this paper constructs a new energy vehicle purchase behavior prediction model from the review text, and explores the predictive effect of consumer personal factors on the purchase behavior of new energy vehicles.

However, getting opinion information from an unstructured review text is quite difficult.

The study employs an analytical methodology using content analysis to review textual data of the Qur’an, Hadith, reports, and articles.

Therefore, this paper proposes a hotel selection model based on Probabilistic linguistic Term Set (PLTS) which integrates online ratings and reviews from multiple websites: (1) Unifying the rating information’s evaluation attributes among different websites based on the PLTS similarity calculation method, putting forward the transformation method of linguistic scale to unify the rating information’s evaluation scale among different websites; (2) Analyzing the sentiment of review texts and putting forward the aggregation model of user reviews based on different groups' risk attitudes; (3) Improving the linguistic scale function to introduce the unbalanced effect of positive and negative evaluations; (4) According to preference differences among different groups, putting forward the attribute weight calculation method and providing recommendation results for different groups.

In this paper, we propose a personalized medical recommendation method based on a convolutional neural network that integrates revised ratings and review text, called revised rating and review based on a convolutional neural network (RR&R-CNN).

Recent studies show that existing methods fail at this task, since review texts usually contain informal language, contain grammatical and spelling errors, as well as the difficulty in filtering out irrelevant information that has no practical value for developers.

From the experiments, given review texts, we demonstrate to build the model to extract the desired entity,i.

We classified the beauty product review texts as spam and non-spam reviews.

Sentiment analysis is a study to classify a review text to sentiment classes Tables.

Aspect term extraction aims to identify the review text span that contains the aspect mentions.

The sample included 572 wineries from all 13 German wine regions with website text data and online review text data from each winery.

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.

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.

This research is based on the curiosity of researchers in the use of think talk write method towards review text learning in class VIII Mts Asaasuttarbiyah Mande.

In this method, we transformed review texts from original discrete time slices to discrete random features, extracted product features based on the constructed feature and sentiment dictionaries, and matched pairs of features and sentiment phrases in the dictionaries.

This research is examined through qualitative approach combining observation and review texts.

The dataset is a clothing review text data taken from Kaggle.

The purpose of this paper is to aggregate the topic information of online review text and clarify the user needs.

The algorithms and knowledge graphs used for generating explanations have not utilized review text.

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.

This research aims to understand the effect of Reciprocal Teaching Strategy on students’ ability of writing review text.

Experiments A are used to identify the regression algorithm used in our model, Experiments B are used to identify the model to analyze review texts and the algorithm to detect social communities, and Experiments C compare our hybrid recommendation model with conventional recommendation models, such as probabilistic matrix factorization, UserKNN, ItemKNN, and social network-based models, such as socialMF and TrustSVD.