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The knowledge obtained from emotions via online communities is substantially valuable in various domains, including social management, resource planning, politics, and market predictions. Affective computing, as a multi-aspect realm, aims to exploit emotion-pertinent details from various contents via connecting artificial intelligence to cognitive science. The hidden personality cues in daily brief contents can reveal the cognitive aspect of authors and uncover both similarities and contrasts between them. However, the main challenge lies in devising a cognition-aware algorithm to trace emotional cues in brief contents. To solve the challenge, we develop a novel framework that infers the cognitive aspect of individuals. We propose a deep ensemble method, supplied with a novel dropout algorithm, that aggregates outcomes from various classifiers to extract emotions from short texts. We employ a new embedding approach to enrich emotion-relevant features, collectively assembled via lexicons and attention actuates, resulting in a preferable set of vectors. The experimental results show that our proposed framework can achieve better accuracy in recognizing emotions versus other trending competitors. We empirically observe that detecting emotion latent cues via relying on personality features can effectively distinguish short text authors. Furthermore, the deep learning models overcome conventional methods, including the SVM, categorization, and heuristic rules. [ABSTRACT FROM AUTHOR]

In recent years, with the increasing volume of databases, the removal of redundant features has become an essential thing in classification. A smaller subset of features is selected using feature selection algorithm. One of the famous algorithms of feature selection methods is the crow search algorithm (CSA). This algorithm's popularity can be mentioned in the algorithm's implementation and process and the impressive results compared to the previous algorithms. Despite all these benefits, this algorithm suffers from problems such as unbalanced global and local search. It is also stuck in local optimization due to the search approach's inadequacy. In this paper, a new algorithm based on CSA is introduced. In order to overcome the shortcoming, four fundamental changes have been made to CSA. (i) The algorithm uses the concept of dynamic awareness probability to solve the balance between exploitation and exploration. Then, a new approach is introduced for each part of the search that improves crows' search performance both (ii) locally and (iii) globally. Also, as the last change, (iv) the concept of chaos is used to increase the algorithm's convergence rate. The proposed method has been tested and compared with ten well-known algorithms in this field on the same datasets and has performed on average 20% better in the feature reduction index and 2.5% in the fitness index, while has a lower performance in accuracy by only 1.5%. Practical results show that the algorithm changes have provided attractive results compared to other algorithms in this field in the mentioned metrics. [ABSTRACT FROM AUTHOR]