Abstract
Only the label corresponding to the maximum value of the fully connected layer is used as the output category when a neural network performs classification tasks. When the maximum value of the fully connected layer is close to the sub-maximum value, the classification obtained by considering only the maximum value and ignoring the sub-maximum value is not completely accurate. To reduce the noise and improve classification accuracy, combining the principles of fuzzy reasoning, this paper integrates all the output results of the fully connected layer with the emotional tendency of the text based on the dictionary to establish a multi-modal fuzzy recognition emotion enhancement model. The provided model considers the enhancement effect of negative words, degree adverbs, exclamation marks, and question marks based on the smallest subtree on the emotion of emotional words, and defines the global emotional membership function of emojis based on the corpus. Through comparing the results of CNN, LSTM, BiLSTM and GRU on Weibo and Douyin, it is shown that the provided model can effectively improve the text emotion recognition when the neural network output result is not clear, especially for long texts.
Highlights
People express their emotions in many ways, such as text [1,2], voice [3], intonation [4], facial expressions [5,6] and the multimodal combination [7]
This paper proposes a text emotion enhancement model based on the smallest subtree, discusses the emotion enhancement effects of negative words and degree adverbs on emotion words, and considers the enhancement of text emotion by question marks and exclamation points
The provided algorithms are listed as follows, where deep neural networks (DP) is replaced by convolutional neural networks (CNN), long short-term memory (LSTM), bi-directional long short-term memory (BiLSTM) or gate recurrent unit (GRU) in the following experimental results
Summary
People express their emotions in many ways, such as text [1,2], voice [3], intonation [4], facial expressions [5,6] and the multimodal combination [7]. Since fuzzy reasoning can deal with the fuzzy expression of language [11,12], and optimize the model without coupling relationship between components [13,14,15], we combine the emotional word dictionary, emoticon dictionary and deep learning model to establish an fuzzy recognition emotion enhancement reasoning model that optimizes the output results of the deep neural networks. (2) Establish a lexicon-based fuzzy inference emotional enhancement model which consists of the emotional enhancement of negative words and degree adverbs on their modified emotional words and the enhancement of question marks and exclamation marks on text emotions. (4) The fuzzy classification membership function of the fully connected layer of the neural network, the emotional membership of emojis and the dictionary-based fuzzy inference model are combined to establish a multimodal and multi-scale emotion-enhanced inference model based on fuzzy recognition.
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