scholarly journals Improving Sentiment Classification of Restaurant Reviews with Attention-Based Bi-GRU Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1517
Author(s):  
Liangqiang Li ◽  
Liang Yang ◽  
Yuyang Zeng
Keyword(s):  
Sentiment Analysis ◽  
Analysis Model ◽  
Huge Amount ◽  
Personalized Services ◽  
Restaurant Reviews ◽  
Hidden Layer ◽  
Analysis Models ◽  
Better Than ◽  
Extract Information

In the era of Web 2.0, there is a huge amount of user-generated content, but the huge amount of unstructured data makes it difficult for merchants to provide personalized services and for users to extract information efficiently, so it is necessary to perform sentiment analysis for restaurant reviews. The significant advantage of Bi-GRU is the guaranteed symmetry of the hidden layer weight update, to take into account the context in online restaurant reviews and to obtain better results with fewer parameters, so we combined Word2vec, Bi-GRU, and Attention method to build a sentiment analysis model for online restaurant reviews. Restaurant reviews from Dianping.com were used to train and validate the model. With F1-score greater than 89%, we can conclude that the comprehensive performance of the Word2vec+Bi-GRU+Attention sentiment analysis model is better than the commonly used sentiment analysis models. We applied deep learning methods to review sentiment analysis in online food ordering platforms to improve the performance of sentiment analysis in the restaurant review domain.

scholarly journals Sentiment analysis of MOOC reviews via ALBERT-BiLSTM model

2021 ◽  
Vol 336 ◽  
pp. 05008
Author(s):  
Cheng Wang ◽  
Sirui Huang ◽  
Ya Zhou
Keyword(s):  
Sentiment Analysis ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Analysis Model ◽  
Accuracy Rate ◽  
Massive Open Online ◽  
Data Set ◽  
Proposed Model ◽  
Contextual Feature

The accurate exploration of the sentiment information in comments for Massive Open Online Courses (MOOC) courses plays an important role in improving its curricular quality and promoting MOOC platform’s sustainable development. At present, most of the sentiment analyses of comments for MOOC courses are actually studies in the extensive sense, while relatively less attention is paid to such intensive issues as the polysemous word and the familiar word with an upgraded significance, which results in a low accuracy rate of the sentiment analysis model that is used to identify the genuine sentiment tendency of course comments. For this reason, this paper proposed an ALBERT-BiLSTM model for sentiment analysis of comments for MOOC courses. Firstly, ALBERT was used to dynamically generate word vectors. Secondly, the contextual feature vectors were obtained through BiLSTM pre-sequence and post-sequence, and the attention mechanism that could calculate the weight of different words in a sentence was applied together. Finally, the BiLSTM output vectors were input into Softmax for the classification of sentiments and prediction of the sentimental tendency. The experiment was performed based on the genuine data set of comments for MOOC courses. It was proved in the result that the proposed model was higher in accuracy rate than the already existing models.

scholarly journals When Word Embeddings Become Endangered

2021 ◽  
pp. 275-288
Author(s):  
Khalid Alnajjar
Keyword(s):  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Endangered Languages ◽  
Word Embeddings ◽  
Analysis Model ◽  
Resource Poor ◽  
Semantic Spaces ◽  
Cross Lingual ◽  
Analysis Models

Big languages such as English and Finnish have many natural language processing (NLP) resources and models, but this is not the case for low-resourced and endangered languages as such resources are so scarce despite the great advantages they would provide for the language communities. The most common types of resources available for low-resourced and endangered languages are translation dictionaries and universal dependencies. In this paper, we present a method for constructing word embeddings for endangered languages using existing word embeddings of different resource-rich languages and the translation dictionaries of resource-poor languages. Thereafter, the embeddings are fine-tuned using the sentences in the universal dependencies and aligned to match the semantic spaces of the big languages; resulting in cross-lingual embeddings. The endangered languages we work with here are Erzya, Moksha, Komi-Zyrian and Skolt Sami. Furthermore, we build a universal sentiment analysis model for all the languages that are part of this study, whether endangered or not, by utilizing cross-lingual word embeddings. The evaluation conducted shows that our word embeddings for endangered languages are well-aligned with the resource-rich languages, and they are suitable for training task-specific models as demonstrated by our sentiment analysis models which achieved high accuracies. All our cross-lingual word embeddings and sentiment analysis models will be released openly via an easy-to-use Python library.

scholarly journals Improving Document-Level Sentiment Classification Using Importance of Sentences

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1336
Author(s):  
Gihyeon Choi ◽  
Shinhyeok Oh ◽  
Harksoo Kim
Keyword(s):  
Sentiment Analysis ◽  
Deep Neural Networks ◽  
Sentiment Classification ◽  
Classification Model ◽  
Classification Task ◽  
Analysis Model ◽  
Proposed Model ◽  
Previous State ◽  
Restaurant Reviews ◽  
Document Level

Previous researchers have considered sentiment analysis as a document classification task, in which input documents are classified into predefined sentiment classes. Although there are sentences in a document that support important evidences for sentiment analysis and sentences that do not, they have treated the document as a bag of sentences. In other words, they have not considered the importance of each sentence in the document. To effectively determine polarity of a document, each sentence in the document should be dealt with different degrees of importance. To address this problem, we propose a document-level sentence classification model based on deep neural networks, in which the importance degrees of sentences in documents are automatically determined through gate mechanisms. To verify our new sentiment analysis model, we conducted experiments using the sentiment datasets in the four different domains such as movie reviews, hotel reviews, restaurant reviews, and music reviews. In the experiments, the proposed model outperformed previous state-of-the-art models that do not consider importance differences of sentences in a document. The experimental results show that the importance of sentences should be considered in a document-level sentiment classification task.

scholarly journals Usense: A User Sentiment Analysis Model for Movie Reviews by Applying LSTM

2020 ◽  
pp. 369-372
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Linear Kernel ◽  
Analysis Model ◽  
Large Dataset ◽  
Major Drawback ◽  
User Comments ◽  
Processing Techniques ◽  
Better Than

Sentiment analysis is a field which deals with assessing the sentiments or emotions of the users on products and services. It takes user comments as input and applies natural language processing techniques to identify the mood of the user. Usually a sentiment is deemed to be positive, negative or neutral depending upon the mood that he expresses in the comments or feedbacks. It is largely used by businesses to improve products and services and also to present its customers with a set of products and services based on their likes and dislikes. State-of-the-art indicates many techniques have been applied in past such as, linear regression and SVM models. Recurrent Neural Networks (RNNs) have improved the way in which sentiment analysis could be done with greater accuracy, but they suffer from major drawback when applied to longer sentences. This paper proposes a sentiment analysis model using Long ShortTerm Memory (LSTM) based approach , which is a variant of RNNs. LSTMs are good in handling long sentence data. The model is applied to reviews collected from IMDB dataset. It is large dataset that contains 50K reviews. Out of the available reviews 50 % are used for training purpose and 50% are used for testing purpose. The model gives a training accuracy of 92% and validation accuracy of 85% which is neither an over fit nor an under fit. The overall accuracy here is 85%, which seems to be better than some of the existing techniques such as SVM with linear kernel.

Sentiment Analysis Model with Semantic Topic Classification of Reviews

2020 ◽  
Vol 9 (2) ◽  
pp. 69-77
Author(s):  
Myung Jin Lim ◽  
Pankoo Kim ◽  
Ju Hyun Shin
Keyword(s):  
Sentiment Analysis ◽  
Analysis Model

Event Based Sentiment Analysis on Futures Trading

2020 ◽  
Author(s):  
Ritu K. Yadav ◽  
Ashwani Kumar ◽  
A. VINAY KUMAR
Keyword(s):  
Sentiment Analysis ◽  
Training Data ◽  
Event Representation ◽  
Analysis Model ◽  
Event Identification ◽  
Market Sentiment ◽  
Time News ◽  
Event Based ◽  
Analysis Models ◽  
The Impact

Market expectations as well as perception of the investment risks and returns are dependent on information arrivals. News arrival forms the basis for market sentiment, which in turn forms the basis for trading positions. Research in sentiment analysis focuses on quantifying the impact that news has on prevailing market sentiment. However, it is not news but events that impact the market sentiment; and the news is one of the modes to disseminate information about the events. Sentiment analysis must distinguish the events from news and events should be used as the predicting construct for market sentiments. This paper proposes an event-based sentiment analysis model that entails event identification, event-based training data creation, and event representation algorithms. A comparative analysis of news-based and event-based sentiment analysis is done on high-frequency futures trades, using the real-time news as the source of market information. The proposed event-based sentiment analysis performed better than the traditional news-based sentiment analysis when evaluated using both the statistical metrics and simulated trading. This paper presents pivotal research in the direction of event-based sentiment analysis models and its implication on algorithmic trading.

scholarly journals Smart Classifiers Based Classification and Condition Monitoring of Induction Motor Faults

2019 ◽  
Vol 8 (12) ◽  
pp. 1922-1928
Keyword(s):  
Induction Motor ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Fault Classification ◽  
Support Vector ◽  
Huge Amount ◽  
Very High ◽  
Better Than

With the advancements in the field of automation in the industries the use of machines is very high and if the machine which require some rotatory action for the load, the Induction motor comes in to play because of the advantages such as robustness, low maintenance, low cost etc. But with the increase in the dependency over the motors it becomes highly recommended to have machines with reliability because break in the work can lead to huge amount of loss. In order to increase the reliability of the motors predictive maintenance comes into play which requires fault classification or detection which is easily and accurately possible using the Machine Learning algorithms. With the requirements of the present scenario for predictive maintenance, this paper presents the fault classification of induction motor using Support Vector Machine SVM) and K- Nearest Neighbour (KNN) technique of classification. Here in this paper the bearing fault (BF) and broken rotor bar (BRB) fault is considered. The results collected are on the basis of validation and Principle Component Analysis (PCA) technique. And it is found that the SVM technique is better than the KNN for fault classification of Induction motor.

ANALISIS MODEL KEBUTUHAN PERGERAKAN PENUMPANG DAN BARANG BANDARA RAHADI OESMAN KETAPANG

2012 ◽  
Vol 10 (1) ◽  
Author(s):  
. Elsa Trimukti
Keyword(s):  
Growth Rate ◽  
Trend Analysis ◽  
Polynomial Regression ◽  
Air Transport ◽  
Trip Generation ◽  
Analysis Model ◽  
Exponential Regression ◽  
Share Model ◽  
Strategic Role ◽  
Analysis Models

Airport of Rahadi Oesman in Kabupaten Ketapang Kalimantan Barat represent the main and important gate for air transport in Kabupaten Ketapang, where this airport own the strategic role in service activities of this transportation even for domestic transportation or regional. Activity in Airport of Rahadi Oesman in a few this the last year has growth so fast growth, so that felt the infrastructure and also available facility in this time have is not adequate again to support the growth rate of air traffic in this airport. In the plan development of facility of air side and also land side of the airport require to be conducted an analysis model of trip generation or attraction of passenger and goods. These models need for the prediction of mount the growth of passenger and goods/cargo and estimate the amount of passenger and aircraft movement in the future pursuant to aircraft characteristic that to be used. The models used for prediction of passenger and goods in this study are Trend Analysis Models consisted of linear regression trend method, exponential regression trend method, and polynomial regression trend method. Besides model of trend analysis, in this study also analyzed Market Share Model. Result from third model then compared to one another to obtain the most appropriate model. Pursuant to analyses result obtained that the best or most appropriate model is Model of Trend Analysis.Model for the attraction passenger is Y = 21,18X2+ 6181X + 5788 by R2= 0,922.Model for the generation passenger is Y = 128,3X2+ 7515X + 4965 by R2= 0,907.Model for the passenger of transit is Y = 795X2+ 561X + 3361 by R2= 1Model for the cargo movement is Y = 2468X2+ 41054X 28341 by R2= 0,918.

Sentiment Analysis of Customer Feedback on Restaurant Reviews

2019 ◽  
Author(s):  
Spoorthi C ◽  
Dr. Pushpa Ravikumar ◽  
Mr. Adarsh M.J
Keyword(s):  
Sentiment Analysis ◽  
Customer Feedback ◽  
Restaurant Reviews
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