Background and aims The development of vaccines against COVID-19 has been a global purpose since the World Health Organization declared the pandemic. People usually use social media, especially Twitter, to transfer knowledge and beliefs on global concerns like COVID-19-vaccination, hence, Twitter is a good source for investigating public opinions. The present study aimed to assess Persian tweets to (1) analyze Iranian people's view toward COVID-19 vaccination. (2) Compare Iranian views toward a homegrown and imported COVID-19-vaccines. Methods First, a total of 803278 Persian tweets were retrieved from Twitter, mentioning COVIran Barekat (the homegrown vaccine), Pfizer/BioNTech, AstraZeneca/Oxford, Moderna, and Sinopharm (imported vaccines) between April 1, 2021 and September 30, 2021. Then, we identified sentiments of retrieved tweets using a deep learning sentiment analysis model based on CNN-LSTM architecture. Finally, we investigated Iranian views toward COVID-19-vaccination. Results (1) We found a subtle difference in the number of positive sentiments toward the homegrown and foreign vaccines, and the latter had the dominant positive polarity. (2) The negative sentiment regarding homegrown and imported vaccines seems to be increasing in some months. (3) We also observed no significant differences between the percentage of overall positive and negative opinions toward vaccination amongst Iranian people. Conclusions It is worrisome that the negative sentiment toward homegrown and imported vaccines increases in Iran in some months. Since public healthcare agencies aim to increase the uptake of COVID-19 vaccines to end the pandemic, they can focus on social media such as Twitter to promote positive messaging and decrease opposing views.

Objectives The present study aimed to assess Iranian tweets to (1)Analyze Iranian views toward COVID-19-vaccination. (2)Compare Iranian views toward homegrown and imported COVID-19-vaccines. (3)Present an effective model for Sentiment Analysis tasks on critical issues like COVID-19-vaccination. Design or methods Persian tweets were retrieved mentioning homegrown and imported vaccines between 1-April-2021 and 30-September-2021. We identified sentiments of retrieved tweets using a deep learning sentiment analysis model. We used a sarcasm detection model based on Random Forest classifier to discover sarcastic tweets and minimize misclassification. Finally, we investigated Iranian views toward COVID-19-vaccination. Results (1)We found subtle differences in number of positive sentiments toward homegrown and imported vaccines, the latter had dominant positive polarity. (2)Negative sentiments regarding homegrown and imported vaccines increased in some months. (3)We observed no significant differences between percentage of overall positive and negative opinions toward vaccination. Conclusion It is worrisome that negative sentiments toward homegrown and imported vaccines in some months increases in Iran. Health organizations can focus on Twitter to promote positive messaging toward COVID-19-vaccination. Sarcasm detection enabled us detect tweets that ironically stated positive sentiments toward vaccination, it improved accuracy of the sentiment analysis results. Our Sentiment Analysis-Sarcasm Detection model is a reliable tool for classification problems.

With increasing members in social media sites today, people tend to share their views about everything online. It is a convenient way to convey their messages to end users on a specific subject. Sentiment Analysis is a subfield of Natural Language Processing (NLP) that refers to the identification of users’ opinions toward specific topics. It is used in several fields such as marketing, customer services, etc. However, limited works have been done on Persian Sentiment Analysis. On the other hand, deep learning has recently become popular because of its successful role in several Natural Language Processing tasks. The objective of this paper is to propose a novel hybrid deep learning architecture for Persian Sentiment Analysis. According to the proposed model, local features are extracted by Convolutional Neural Networks (CNN) and long-term dependencies are learned by Long Short Term Memory (LSTM). Therefore, the model can harness both CNN's and LSTM's abilities. Furthermore, Word2vec is used for word representation as an unsupervised learning step. To the best of our knowledge, this is the first attempt where a hybrid deep learning model is used for Persian Sentiment Analysis. We evaluate the model on a Persian dataset that is introduced in this study. The experimental results show the effectiveness of the proposed model with an accuracy of 85%. ABSTRAK: Hari ini dengan ahli yang semakin meningkat di laman media sosial, orang cenderung untuk berkongsi pandangan mereka tentang segala-galanya dalam talian. Ini adalah cara mudah untuk menyampaikan mesej mereka kepada pengguna akhir mengenai subjek tertentu. Analisis Sentimen adalah subfield Pemprosesan Bahasa Semula Jadi yang merujuk kepada pengenalan pendapat pengguna ke arah topik tertentu. Ia digunakan dalam beberapa bidang seperti pemasaran, perkhidmatan pelanggan, dan sebagainya. Walau bagaimanapun, kerja-kerja terhad telah dilakukan ke atas Analisis Sentimen Parsi. Sebaliknya, pembelajaran mendalam baru menjadi popular kerana peranannya yang berjaya dalam beberapa tugas Pemprosesan Bahasa Asli (NLP). Objektif makalah ini adalah mencadangkan senibina pembelajaran hibrid yang baru dalam Analisis Sentimen Parsi. Menurut model yang dicadangkan, ciri-ciri tempatan ditangkap oleh Rangkaian Neural Convolutional (CNN) dan ketergantungan jangka panjang dipelajari oleh Long Short Term Memory (LSTM). Oleh itu, model boleh memanfaatkan kebolehan CNN dan LSTM. Selain itu, Word2vec digunakan untuk perwakilan perkataan sebagai langkah pembelajaran tanpa pengawasan. Untuk pengetahuan yang terbaik, ini adalah percubaan pertama di mana model pembelajaran mendalam hibrid digunakan untuk Analisis Sentimen Persia. Kami menilai model pada dataset Persia yang memperkenalkan dalam kajian ini. Keputusan eksperimen menunjukkan keberkesanan model yang dicadangkan dengan ketepatan 85%.

Sarcasm is a form of communication where the individual states the opposite of what is implied. Therefore, detecting a sarcastic tone is somewhat complicated due to its ambiguous nature. On the other hand, identification of sarcasm is vital to various natural language processing tasks such as sentiment analysis and text summarisation. However, research on sarcasm detection in Persian is very limited. This paper investigated the sarcasm detection technique on Persian tweets by combining deep learning-based and machine learning-based approaches. Four sets of features that cover different types of sarcasm were proposed, namely deep polarity, sentiment, part of speech, and punctuation features. These features were utilised to classify the tweets as sarcastic and nonsarcastic. In this study, the deep polarity feature was proposed by conducting a sentiment analysis using deep neural network architecture. In addition, to extract the sentiment feature, a Persian sentiment dictionary was developed, which consisted of four sentiment categories. The study also used a new Persian proverb dictionary in the preparation step to enhance the accuracy of the proposed model. The performance of the model is analysed using several standard machine learning algorithms. The results of the experiment showed that the method outperformed the baseline method and reached an accuracy of 80.82%. The study also examined the importance of each proposed feature set and evaluated its added value to the classification.