Computational Intelligence
ramin mousa; Mohammad Ali Dadgostarnia; Amir Olfati Malamiri; Elham Behnam; Shahram Miri Kelaniki
Abstract
Sentiment Analysis (SA) is the computational analysis of ideas, feelings and opinions using natural language processing techniques, computational methods and text analysis to extract polarity (positive, negative or neutral) from unstructured documents or textual comments. Multi-domain SA is based on ...
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Sentiment Analysis (SA) is the computational analysis of ideas, feelings and opinions using natural language processing techniques, computational methods and text analysis to extract polarity (positive, negative or neutral) from unstructured documents or textual comments. Multi-domain SA is based on a labelled dataset, which reduces the dependence on large amounts of domain-specific data and addresses data scarcity issues by leveraging existing data from other domains. This paper presents a novel deep learning-based approach for Persian multi-domain SA analysis. The proposed Bi-IndRNNCapsule technique combines bidirectional IndRNN and CapsuleNet, which use Bi-GRU to extract features for CapsuleNet. In IndRNN, recurrent layer neurons operate independently, with simple RNN computing the hidden state h via element-wise vector multiplication u * state, indicating that each neuron has a solitary recurrent weight linking it to the most recent hidden state. We evaluated the proposed approach on the Digikala dataset and found it to provide acceptable accuracy compared to existing techniques.
Other
seyed sadegh hosseini; Mohammadreza Yamaghani; Soodabeh Poorzaker Arabani
Abstract
Emotional computing synergizes the understanding and quantification of emotions, drawing on diverse data sources such as text, audio, and visual indicators. A challenge arises when attempting to discern authentic emotions from those concealed deliberately via facial cues, vocal nuances, and other communicative ...
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Emotional computing synergizes the understanding and quantification of emotions, drawing on diverse data sources such as text, audio, and visual indicators. A challenge arises when attempting to discern authentic emotions from those concealed deliberately via facial cues, vocal nuances, and other communicative behaviours. By integrating multiple physiological and behavioural signals, more profound insights into an individual's emotional state can be achieved. Historically, research has predominantly concentrated on a singular facet of emotional computing. In contrast, our study offers an in-depth exploration of its pivotal domains, encompassing emotional models, Databases (DBs), and contemporary developments. We commence by elucidating two prevalent emotional models, followed by an examination of a renowned sentiment analysis DB. Subsequently, we delve into cutting-edge methodologies for emotion detection and analysis across varied sensory channels, elaborating on their design and operational principles. In conclusion, the fundamental principles of emotional computing and its real-world implications are discussed. This review endeavours to provide researchers from academia and industry with a holistic understanding of the latest progress in this domain.
Data mining
Aboosaleh Mohammad Sharifi; Kaveh Khalili Damghani; Farshid Abdi; Soheila Sardar
Abstract
Cryptocurrencies are considered as new financial and economic tools having special and innovative features, among which Bitcoin is the most popular. The contribution of the Bitcoin market continues to grow due to the special nature of Bitcoin. The investors' attention to Bitcoin has increased significantly ...
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Cryptocurrencies are considered as new financial and economic tools having special and innovative features, among which Bitcoin is the most popular. The contribution of the Bitcoin market continues to grow due to the special nature of Bitcoin. The investors' attention to Bitcoin has increased significantly in recent years due to significant growth in its prices. It is important to create a prediction system which works well for investment management and business strategies due to the high chaos and volatility of Bitcoin prices. In this study, in order to improve predictive accuracy, Bitcoin price dataset is first divided into a time interval through time window, then propose a new model based on Long Short-Term Memory (LSTM) neural networks and Metaheuristic algorithms. Chaotic Dolphin Swarm Optimization algorithm is used to optimize the LSTM. Performance evaluation indicated that the proposed model can have more effective predictions and improve prediction accuracy. In addition, the performance of the optimized model is better and more reliable than other models.
Decision analysis and methods
Vahid Mottaghi; Mahdi Esmaeili; Ghasem Ali Bazaee; Mohammadali Afshar Kazemi
Abstract
With the increase of news on social networks, a way to identify fake news has become an essential matter. Classification is a fundamental task in natural language processing (NLP). Convolutional neural network (CNN), as a popular deep learning model, has shown remarkable success in the task of fake news ...
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With the increase of news on social networks, a way to identify fake news has become an essential matter. Classification is a fundamental task in natural language processing (NLP). Convolutional neural network (CNN), as a popular deep learning model, has shown remarkable success in the task of fake news classification. In this paper, new baseline models were studied for fake news classification using CNN. In these models, documents are fed to the network as a 3-dimensional tensor representation to provide sentence-level analysis. Applying such a method enables the models to take advantage of the positional information of the sentences in the texts. Besides, analyzing adjacent sentences allows extracting additional features. The proposed models were compared with the state-of-the-art models using a collection of real and fake news extracted from Twitter about covid-19, and the fusion layer was used as the decision layer in selecting the best feature. The results showed that the proposed models had better performance, particularly in these documents, and the results were obtained with 97.33% accuracy for classification on Covid-19 after reviewing the evaluation criteria of the proposed decision system model.