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CNN–BiLSTM

CNN–BiLSTM

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The hybrid neural network model proposed in this paper consists of two main parts: extracting local features of text vectors by convolutional neural network, extracting global features related to text context by BiLSTM, and fusing the features extracted by the two complementary models. In this paper, the pre-processed sentences are put into the hyb...

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... A commonly used clustering algorithm in various fields is based on the partitioning method. It is not easy to obtain valuable information from the fragmented student information by traditional methods, so the fragmented student psychological data are formed into multiple correlation infographics according to the associated attributes [4]. By using cluster analysis in the student psychological education system, we can analyze the potential value of information on student psychology and the correlation between each information factor from the vast amount of student psychometric data in a school database and provide more scientific solutions for student mental health to the general psychological teaching staff. ...
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... Interpersonal communication is the starting point of socialization. Positive interpersonal communication helps college students obtain richer information and maintain contact with society; positive and harmonious interpersonal relationships also help college students develop good personalities [13]. e system of college students' mental health assessment and analysis and the principle of sexuality require the model to comprehensively and systematically find out the factors that affect the mental health of students from a systematic perspective, comprehensively using qualitative knowledge such as psychology and pedagogy. ...
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... Deep learning architectures significantly outperform classical machine learning methods in most NLP tasks off late. Deep learning based works in textual emotion detection includes CNNs [42], combination of CNN with various RNN models [15,43], stacked RNNs [44], attention-based architectures [45], Gated Recurrent Unit (GRU) [46], LSTM [47], etc. Apart from these studies, Kratzwald et al. [48] and Chatterjee et al. [44] consider the possibilities of sentiment aided transfer learning (sent2affect) and sentimentspecific word embedding (SS-BED), respectively, for textual emotion detection. Research in textual emotion detection specific to readers' emotions also explore similar learning architectures [9,14,49]. ...
... Slightly different lines of inquiry to predict readers' emotions are presented in recent works, viz., [50] that utilize an ontology driven knowledge base with deep learning classifier and [51] that combines comments along with articles as input to their deep learning model. In reference to such recent advances, we draw upon the notable studies sent2affect [48] and SS-BED [44], and the RNN architectures, GRU [46], LSTM and Bi-LSTM [15,44,48], as baselines in our empirical evaluation. ...
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