The latent knowledge in the emotions and the opinions of the individuals that are manifested via social networks are crucial to numerous applications including social management, dynamical processes, and public security. Affective computing, as an interdisciplinary research field, linking artificial intelligence to cognitive inference, is capable to exploit emotion-oriented knowledge from brief contents. The textual contents convey hidden information such as personality and cognition about corresponding authors that can determine both correlations and variations between users. Emotion recognition from brief contents should embrace the contrast between authors where the differences in personality and cognition can be traced within emotional expressions. To tackle this challenge, we devise a framework that, on the one hand, infers latent individual aspects, from brief contents and, on the other hand, presents a novel ensemble classifier equipped with dynamic dropout convnets to extract emotions from textual context. To categorize short text contents, our proposed method conjointly leverages cognitive factors and exploits hidden information. We utilize the outcome vectors in a novel embedding model to foster emotion-pertinent features that are collectively assembled by lexicon inductions. Experimental results show that compared to other competitors, our proposed model can achieve a higher performance in recognizing emotion from noisy contents.
Multimodal sentiment analysis benefits various applications such as human-computer interaction and recommendation systems. It aims to infer the users' bipolar ideas using visual, textual, and acoustic signals. Although researchers affirm the association between cognitive cues and emotional manifestations, most of the current multimodal approaches in sentiment analysis disregard user-specific aspects. To tackle this issue, we devise a novel method to perform multimodal sentiment prediction using cognitive cues, such as personality. Our framework constructs an adaptive tree by hierarchically dividing users and trains the LSTM-based submodels, utilizing an attention-based fusion to transfer cognitive-oriented knowledge within the tree. Subsequently, the framework consumes the conclusive agglomerative knowledge from the adaptive tree to predict final sentiments. We also devise a dynamic dropout method to facilitate data sharing between neighboring nodes, reducing data sparsity. The empirical results on real-world datasets determine that our proposed model for sentiment prediction can surpass trending rivals. Moreover, compared to other ensemble approaches, the proposed transfer-based algorithm can better utilize the latent cognitive cues and foster the prediction outcomes. Based on the given extrinsic and intrinsic analysis results, we note that compared to other theoretical-based techniques, the proposed hierarchical clustering approach can better group the users within the adaptive tree.
Hypothalamo‐pituitary‐adrenal axis is involved in the stress regulation. Understanding the cellular mechanisms involved in this system is critical in studying stress response. Tyrosine hydroxylase (TH), the rate‐limiting enzyme of catecholamine synthesis, has been suggested to play a pivotal role in regulating the neuronal functions under stress. Previous findings showed the brainstem ascending TH‐immunoreactive fibers participate in the cellular activation of paraventricular nucleus of the hypothalamus in response to neurogenic stress. Current study aimed to study the role of TH in regulating brain areas which are involved in the neural circuits in stress response. Immunocytochemical staining of TH confirmed the TH‐immunoreactive positive neurons are found in the locus coeruleus, ventral lateral medulla, nucleus of the solitary tract and zona incerta; wihile TH‐ir fibers are widely found in the forebrain region including the striatum, paraventricular nucleus of hypothalamus, and amygdala. These are areas rich in neurons bearing dopamine receptor, corticotropin‐releasing factor and oxytocin. Using double immunohistochemical staining, we are able to demonstrate the functional significance of tyrosine hydroxylase inputs to these different populations of neurons.
Support or Funding Information
We are grateful to Oregon Tech for supporting undergraduate research as well as Dr. Paul Sawchenko for his generosity in donating rat brain tissues.
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