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.
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.