A hybrid method for bilingual text sentiment classification based on deep learning

Liu, Guolong; Xu, Xiaofei; Deng, Bailong; Chen, Siding; Li, Li

doi:10.1109/snpd.2016.7515884

Cited by 18 publications

(19 citation statements)

References 9 publications

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“…33 and Fig. 34, we observe that our proposal based on the deep learning model (CNN+FFNN), Hadoop framework, and Mamdani fuzzy system outperforms the other used approaches (Jin et al [29], Lan et al [31], Lin et al [32], Liu et al [33], and Xing et al [36]) with accuracy equal to 99.83%,99.99%, and execution time equal to 0.0089s, and 0.00534 on Sentiment140 dataset and COVID-19_Sentiments dataset respectively. Our proposal's significant effectiveness and performance are due to the application classifier, we did another experiment that compares our proposal with the other selected approaches from the literature (Jin et al [29], Lan et al [31], Lin et al [32], Liu et al [33], and Xing et al [36]).…”

Section: Methodsmentioning

confidence: 76%

“…This paper, called CE-B-MHA, combines multi-Head attention to extracting global features and the strength of Bi-LSTM to discover the local sequence features. A "hybrid method for bilingual text sentiment classification based on deep learning" implemented by Liu et al [33]; this suggested method integrates NB, SVM machine learning algorithm with RNN, and LSTM deep learning model. And finally, "Intelligent asset allocation via market sentiment views" designed by Xing et al [36]; also, this proposed method combines the evolving clustering with LSTM deep learning model.…”

Section: Methodsmentioning

confidence: 99%

“…In this last experiment, we have evaluated the effectiveness of our proposed FDLC in terms of complexity, convergence, and stability. This experiment serves to compare our proposed FDLC, Jin et al [29], Lan et al [31], Lin et al [32], Liu et al [33], and Xing et al [36] and to discover the more efficient method among all evaluated approaches in terms of complexity, convergence, and stability.…”

Section: E Experimentsmentioning

confidence: 99%

“…The complexity of a model is a measurement of the time consumption and space used by a model. in this subsection we evaluated the time complexity and space complexity of [33], and Xing et al [36]. Additionally, Table 14 presents the experimental results obtained after we measure the space complexity of our FDLC model and other chosen models in terms of the number of executed operations, and the number of the network parameters.…”

Section: F Complexitymentioning

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: Methodsmentioning

confidence: 76%

Section: Methodsmentioning

confidence: 99%

Section: E Experimentsmentioning

confidence: 99%

Section: F Complexitymentioning

confidence: 99%

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Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

et al. 2021

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“…Within the scope of these studies, it was discovered that better classification performances were obtained using the hybrid algorithm. Liu et al [6] combined machine learning with deep learning to provide better sentiment classification performance. In their study, the effectiveness of the proposed method is shown on Turkish and Chinese language datasets.…”

Section: Literaturementioning

confidence: 99%

A Novel Hybrid Deep Learning Model for Sentiment Classification

Salur¹,

Aydın²

2020

IEEE Access

158

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A massive use of social media platforms such as Twitter and Facebook by omnifarious organizations has increased the critical individual feedback on the situation, events, products, and services. However, sentiment classification plays an important role in the user's feedback evaluation. At present, deep learning such as long short-term memory (LSTM), gated recurrent unit (GRU), bidirectionally long short-term memory (BiLSTM) or convolutional neural network (CNN) are prevalently preferred in sentiment classification. Moreover, word embedding such as Word2Vec and FastText is closely examined in text for mapping closely related to the vectors of real numbers. However, both deep learning and word embedding methods have strengths and weaknesses. Combining the strengths of the deep learning models with that of word embedding is the key to high-performance sentiment classification in the field of natural language processing (NLP). In the present study, we propose a novel hybrid deep learning model that strategically combines different word embedding (Word2Vec, FastText, character-level embedding) with different deep learning methods (LSTM, GRU, BiLSTM, CNN). The proposed model extracts features of different deep learning methods of word embedding, combines these features and classifies texts in terms of sentiment. To verify the performance of the proposed model, several deep learning models called basic models were created to perform series of experiments. By comparing, the performance of the proposed model with that of past studies, the proposed model offers better sentiment classification performance. INDEX TERMS Sentiment classification, Turkish tweets analysis, hybrid model, word embedding, deep learning, LSTM, CNN.

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