Sentiment Analysis with Tree-Structured Gated Recurrent Units

Kuta, Marcin; Morawiec, Mikołaj; Kitowski, Jacek

doi:10.1007/978-3-319-64206-2_9

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…Content may change prior to final publication. Computation of the recall for multi-class classification is made according to (13). Where tp i is the number of predicted instances are labeled class i,i is the index of the class , l is the total number of class labels, and tp i + f n i is the total number of instances labeled class label i in the given dataset.…”

Section: F Alsen Egativeratementioning

confidence: 99%

“…The F1-Score is computed by applying (20) for multi-class classification. Where P recision i is computed using the formula (18), and Recall i is calculated using the formula (13).…”

Section: F Alsen Egativeratementioning

confidence: 99%

“…Machine Learning (ML) methods for sentiment classification like Support Vector Machines (SVM) [4], K-Nearest Neighbors (KNN) [5], Random Forest (RF) [6], Logistic Regression (LR) [7], Naive Bayesian (NB) [8] or Decision Tree (DT) classifier [9]. Deep Learning (DL) methods like Convolutional Neural Network (CNN) [10], Feedforward Neural Network (FFNN) [11], Long Short-Term Memory (LSTM) [12], Gated Recurrent Unit (GRU) [13], or Recurrent Neural Network (RNN) [14] which are used for automatically extracting features from the given text and for performing the sentiment classification. and vectorization methods like word embeddings (word2vec, glove) [15], tfidf [16], bag-of-words [17], fast-text, n-gram or characterlevel [18], which are used for representing the given text by numerical values.…”

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confidence: 99%

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Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

et al. 2021

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At present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number of social platforms and their extensive use by people, enormous amounts of data are produced hourly. However, sentiment analysis or opinion mining is considered as a useful tool that aims to extract the emotion and attitude from the user-posted data on social media platforms by using different computational methods to linguistic terms and various Natural Language Processing (NLP). Therefore, enhancing text sentiment classification accuracy has become feasible, and an interesting research area for many community researchers. In this study, a new Fuzzy Deep Learning Classifier (FDLC) is suggested for improving the performance of data-sentiment classification. Our proposed FDLC integrates Convolutional Neural Network (CNN) to build an effective automatic process for extracting the features from collected unstructured data and Feedforward Neural Network (FFNN) to compute both positive and negative sentimental scores. Then, we used the Mamdani Fuzzy System (MFS) as a fuzzy classifier to classify the outcomes of the two used deep (CNN+FFNN) learning models in three classes, which are: Neutral, Negative, and Positive. Also, to prevent the long execution time taking by our hybrid proposed FDLC, we have implemented our proposal under the Hadoop cluster. An experimental comparative study between our FDLC and some other suggestions from the literature is performed to demonstrate our offered classifier's effectiveness. The empirical result proved that our FDLC performs better than other classifiers in terms of true positive rate, true negative rate, false positive rate, false negative rate, error rate, precision, classification rate, kappa statistic, F1-score and time consumption, complexity, convergence, and stability.

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Section: F Alsen Egativeratementioning

confidence: 99%

“…The F1-Score is computed by applying (20) for multi-class classification. Where P recision i is computed using the formula (18), and Recall i is calculated using the formula (13).…”

Section: F Alsen Egativeratementioning

confidence: 99%

mentioning

confidence: 99%

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Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

et al. 2021

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“…In this regard, Tai et al [33] employed Long Short Term Memory (LSTM) network integrated with some complex units for sentiment analysis. Kuta et al [34] proposed tree structure gated recurrent neural network which was inspired by tree structure LSTM and adaptation of Gated Recurrent Unit (GRU) to recursive model. Besides these networks, a semi-supervised model, known as the Recursive Neural Network (ReRNN), has been also employed for the task of sentiment analysis which uses continuous word vectors as input and hierarchical structure.…”

Section: B Deep Neural Networkmentioning

confidence: 99%

Multi-View Deep Network: A Deep Model Based on Learning Features From Heterogeneous Neural Networks for Sentiment Analysis

2020

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By the development of social media, sentiment analysis has changed to one of the most remarkable research topics in the field of natural language processing which tries to dig information from textual data containing users' opinions or attitudes toward a particular topic. In this regard, deep neural networks have emerged as promising techniques that have been extensively used for this aim in recent years and obtained significant results. Considering the fact that deep neural networks can automatically extract features from data, it can be claimed that intermediate representations extracted from these networks can be also used as appropriate features. While different deep neural networks are able to extract various types of features due to their distinct structures, we decided to combine features extracted from heterogeneous neural networks using multi-view classifiers to enhance the overall performance of document-level sentiment analysis by considering the correlation between them. The proposed multi-view deep network makes use of intermediate features extracted from convolutional and recursive neural networks to perform classification. Based on the results of the experiments, the proposed multi-view deep network not only outperforms singleview deep neural networks but also has superior efficiency and generalization performance.INDEX TERMS Deep learning, multi-view learning, convolutional neural network, recursive neural network, sentiment analysis.

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“…Autoencoders have been applied to many tasks such as probabilistic and generative modeling [13] and representation learning [14]. A GRU is a Recurrent Neural Network (RNN) that has been used for tasks such as sentiment analysis [15] and speech recognition [16]. The proposed model is evaluated using three labeled log message data sets, namely BlueGene/L (BGL), OpenStack, and Thunderbird.…”

Section: Introductionmentioning

confidence: 99%

Log Message Anomaly Detection with Oversampling

Farzad¹,

Gulliver²

2020

IJAIA

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Imbalanced data is a significant challenge in classification with machine learning algorithms. This is particularly important with log message data as negative logs are sparse so this data is typically imbalanced. In this paper, a model to generate text log messages is proposed which employs a SeqGAN network. An Autoencoder is used for feature extraction and anomaly detection is done using a GRU network. The proposed model is evaluated with three imbalanced log data sets, namely BGL, OpenStack, and Thunderbird. Results are presented which show that appropriate oversampling and data balancing improves anomaly detection accuracy.

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Sentiment Analysis with Tree-Structured Gated Recurrent Units

Cited by 9 publications

References 9 publications

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

Multi-View Deep Network: A Deep Model Based on Learning Features From Heterogeneous Neural Networks for Sentiment Analysis

Log Message Anomaly Detection with Oversampling

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