mohammadjavad shayegan

In recent years, scholars have dedicated significant attention to the field of sentiment analysis. A substantial volume of feedback shared by tourists on social networking platforms, notably on Tripadvisor, manifests as reviews. The tourism sector stands to gain valuable insights from sentiment analysis applied to such reviews. Despite the extensive body of research in sentiment analysis, scant attention has been directed toward multilingual sentiment analysis, particularly within the domain of tourism. This is noteworthy given the inherently multilingual and global nature of the tourism industry. This study aims to address this gap by presenting a comprehensive multilingual sentiment analysis conducted on Tripadvisor reviews. The sentiment analysis model is crafted using various layers of a neural network. We introduce an augmented Attention-based Bidirectional CNN-RNN Deep Model (Extended ABCDM). Comparative analysis reveals that the multilingual model attains a superior F1 measure of 0.732, outperforming previous models.

Sarcasm is a form of speech in which a person expresses his opinion implicitly. We may encounter a seemingly positive sentence in sarcasm, but the speaker has a contrary opinion. Sarcasm can be recognized in spoken language based on body language and the tone of voice. However, the lack of these features makes it difficult to recognize sarcasm in text. In recent years, Twitter has attracted much attention and has become a popular platform for sharing opinions and viewpoints. It is also common for people to use sarcasm on Twitter as an indirect means of expressing their opinions. The presence of sarcasm in the text makes it difficult to recognize the sentiment. Thus, it is necessary and inevitable to have solutions that can detect sarcasm. This study aims to provide a solution for detecting sarcasm on Twitter using deep learning approaches. This study used two Twitter datasets containing balance and imbalance data for modeling. The main idea of this research is to use additional features such as sentimental features, subjectivity, number of hashtags, and punctuation along with features that deep learning algorithms automatically extract. The impact of each feature is reported in the paper. In this research, GRU-Capsule based neural network has been used. According to the results, the proposed model has improved accuracy by 5% for balanced data and by 2% for imbalanced data.

Today, social networks play a crucial role in disseminating information worldwide. Twitter is one of the most popular social networks, with 500 million tweets sent on a daily basis. The popularity of this network among users has led spammers to exploit it for distributing spam posts. This paper employs a combination of machine learning methods to identify spam at the tweet level. The proposed method utilizes a feature extraction framework in two stages. In the first stage, Stacked Autoencoder is used for feature extraction, and in the second stage, the extracted features from the last layer of Stacked Autoencoder are fed into the softmax layer for prediction. The proposed method is compared and evaluated against some popular methods on the Twitter Spam Detection corpus using accuracy, precision, recall, and F1-score metrics. The research results indicate that the proposed method achieves a detection of 78.1%. Overall, the proposed method, using the majority voting approach with a hard selection in ensemble learning, outperforms CNN, LSTM, and SCCL methods in identifying spam tweets with higher accuracy.

The success of e-learning is still a challenging issue. This study presents a model for the evaluation of the success of e-learning in three different faculties. More specifically, the present article answers the question of whether e-learning success variables are different in different faculties. The method of this study was descriptive-survey research. Evaluating research validity was conducted through confirmatory factor analysis, and Cronbach's alpha was used to measure the reliability of the research instrument. The method of the structural equation was used for modeling. The findings reveal that the students’ opinions in the three faculties about the success variables were significantly different. In the Faculty of Engineering, teaching with a coefficient of 0.93, in the Humanities, service quality with a coefficient of 0.9, and in the Arts Faculty, support quality with a coefficient of 0.82 were identified as the highest impact factors. On the other hand, significant commonalities were observed.

Today's world is struggling with the COVID-19 pandemic, as one of the greatest challenges of the 21st century. During the lockdown caused by this disease, many financial losses have been inflicted on people and all industries. One of the fastest ways to save these industries from the COVID-19 or any possible pandemic in the future is to provide a reliable, fast, smart, and secure solution for people's health assessment. In this article, blockchain technology is used to propose a model which provides and validates the health certificates for people who travel or present in society.  For this purpose, we take advantage of blockchain features such as being unchangeable, errorless, distributed, and a single point of failure nonexistence, high security, and proper use in protecting people's privacy. Since a variety of antibody and human health proving tests against the virus are developing, this study tries simultaneously to design an integrated and secure system to meet the authenticity and accuracy of different people's health certificates for the companies requiring these certifications. In this system, on the one hand, there are qualified laboratories that are responsible for performing standard testing and also providing results to the system controller. On the other hand, entities that need to receive health certificates must be members of this system. Finally, people are considered as the end-user of the system. To provide test information for the entities, the mechanism of KYC tokens will be used based on the Stellar private blockchain network. In this mechanism, the user will buy a certain amount of KYC tokens from the system controller. These tokens are charged in the user's wallet, and the user can send these tokens from his wallet to any destination company, to exchange the encrypted health certificate information. Finally, considering the appropriate platform provided by blockchain technology and the requirement of a reliable and accurate solution for issuing health certificates during the Covid-19 pandemic or any other disease, this article offers a solution to meet the requirements.