Multi-attention fusion modeling for sentiment analysis of educational big data

Zhai, Guanlin; Yang, Yan; Wang, Heng; Du, Sichun

doi:10.26599/bdma.2020.9020024

Cited by 66 publications

(30 citation statements)

References 21 publications

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“…The standard learner starting parameters are learned to adapt to fast gradient changes, and the LSTM metalearner is trained as an optimization method customized to the metalearning job. Authors [ 14 ] investigated a particular example, in which the metalearner must update the classifier using gradient descent, and showed that a reduced model (dubbed MAML) may obtain equal results. Authors [ 15 ] investigated a more complex weight updating approach that yielded slight improvements in few-shot classification performance.…”

Section: Related Workmentioning

confidence: 99%

Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions

Bhende¹,

Thakare²,

Pant

et al. 2022

Computational Intelligence and Neuroscience

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With the continuous development of social networks, Weibo has become an essential platform for people to share their opinions and feelings in daily life. Analysis of users’ emotional tendencies can be effectively applied to public opinion control, public opinion surveys, and product recommendations. However, the traditional deep learning algorithm often needs a large amount of data to be retained to obtain a better accuracy when faced with new work tasks. Given this situation, a multiclassification method of microblog negative sentiment based on MAML (model-agnostic metalearning) and BiLSTM (bidirectional extended short-term memory network) is proposed to represent the microblog text word vectorization and the combination of MAML and BiLSTM is constructed. The model of BiLSTM realizes the classification of negative emotions on Weibo and updates the parameters through machine gradient descent; the metalearner in MAML calculates the sum of the losses of multiple pieces of training, performs a second gradient descent, and updates the metalearner parameters. The updated metalearner can quickly iterate when faced with a new Weibo negative sentiment classification task. The experimental results show that compared with the prepopular model, on the Weibo negative sentiment dataset, the precision rate, recall rate, and F1 value are increased by 1.68 percentage points, 2.86 percentage points, and 2.27 percentage points, respectively.

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Section: Related Workmentioning

confidence: 99%

Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions

Bhende¹,

Thakare²,

Pant

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The test results for the Restaurant dataset reached an F1 value of 65.20%, the Laptop dataset only achieved an F1 value of 55.08%, and the Twitter dataset achieved the lowest F1 value (just 47.89%). Zhai et al 23 encountered a similar situation when proposing a model combining many LSTM modules to conduct sentiment analysis for Course, Education, and Restaurant datasets. This model achieved an accuracy of 94.6% on the Education dataset.…”

Section: Introductionmentioning

confidence: 97%

Embedding knowledge on ontology into the corpus by topic to improve the performance of deep learning methods in sentiment analysis

Nguyễn

Phan

2021

Sci Rep

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Sentiment classification, which uses deep learning algorithms, has achieved good results when tested with popular datasets. However, it will be challenging to build a corpus on new topics to train machine learning algorithms in sentiment classification with high confidence. This study proposes a method that processes embedding knowledge in the ontology of opinion datasets called knowledge processing and representation based on ontology (KPRO) to represent the significant features of the dataset into the word embedding layer of deep learning algorithms in sentiment classification. Unlike the methods that lexical encode or add information to the corpus, this method adds presentation of raw data based on the expert’s knowledge in the ontology. Once the data has a rich knowledge of the topic, the efficiency of the machine learning algorithms is significantly enhanced. Thus, this method is appliable to embed knowledge in datasets in other languages. The test results show that deep learning methods achieved considerably higher accuracy when trained with the KPRO method’s dataset than when trained with datasets not processed by this method. Therefore, this method is a novel approach to improve the accuracy of deep learning algorithms and increase the reliability of new datasets, thus making them ready for mining.

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“…The major aim of sentiment analysis [4] is to discover the attitude of bipolar prospects on specific targets in a sentence [41]. In recent years, diverse approaches have been proposed for sentiment analysis by considering the complex text polarity [44]. Several researchers focus on knowing about the sentiments of the texts in various constraints [32].…”

Section: Introductionmentioning

confidence: 99%

SenDemonNet: sentiment analysis for demonetization tweets using heuristic deep neural network

Kayıkçı

2022

Multimed Tools Appl

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Sentiment analysis is one of the efficient models for extracting opinion mining with identification and classification from unstructured text data such as product reviews or microblogs. It is used to gain feedback from political campaigns, brand reviews, marketing analysis, and customers. The sentiment analysis on Twitter data is a recent research field in the natural processing. The dataset is gathered from the "Twitter" package in R along with Twitter API. The main intent of this paper is to understand the public opinion on the recently implemented demonetization policy using the proposed SenDemonNet. Initially, the tweet preprocessing was done, which is intended for cleaning the text data. Then, the feature extraction is performed by Bag of n-grams, TF-IDF, and the word2vec algorithm. The main objective of this work is a weighted feature selection that is developed by the hybrid Forest-Whale Optimization Algorithm (F-WOA) to get the best classification outcome. With these features, the Heuristic Deep Neural Network (HDNN) is adopted for classification, where the proposed FOA and WOA tune the parameter of DNN for reaching the maximum accuracy rate. From the statistical analysis, the performance of the designed F-WOA-DNN is 1.8%, 1.9%, 1.86%, and 2% enhanced than PSO-DNN, GWO-DNN, WOA-DNN, FOA-DNN, SVM, CNN, LSTM, and DNN respectively. Extensive experimental results show that SenDemonNet outperforms its competitors, producing an impressive increase in the classification accuracy on the benchmark dataset.

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Multi-attention fusion modeling for sentiment analysis of educational big data

Cited by 66 publications

References 21 publications

Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions

Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions

Embedding knowledge on ontology into the corpus by topic to improve the performance of deep learning methods in sentiment analysis

SenDemonNet: sentiment analysis for demonetization tweets using heuristic deep neural network

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