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Named Entity Recognition (NER) is an important natural language processing (NLP) tool for information extraction and retrieval from unstructured texts such as newspapers, blogs and emails. NER involves processing unstructured text for classification of words or expressions into relevant categories. In literature, NER has been developed for various languages but limited work has been conducted to develop NER for Persian text. This is due to limited resources (such as corpus, lexicons etc.) and tools for Persian named entities. In this paper, a novel scalable system for Persian Named Entity Recognition (PNER) is presented. The proposed PNER can recognize and extract three most important named entities in Persian script: the person name, location and date. The proposed PNER has been developed by combining a grammatical rule-based approach with machine learning. The proposed framework has integrated dictionaries of Persian named entities, Persian grammar rules and a Support Vector Machine (SVM). The performance evaluation of PNER in terms of precision, recall and f-measure has achieved comparable results with the state-of-the-art NER frameworks in other languages.

People need to know other people’s opinions to make well-informed decisions to buy products or services. Companies and organizations need to understand people’s attitude towards their products and services and use feedback from the customers to improve their products. Sentiment analysis techniques address these needs. While the majority of Internet users are not English speakers, most research papers in the sentiment-analysis field focus on English; resources for other languages are scarce. In this paper, we introduce a Persian sentiment lexicon, which consists of 1500 words along with their part-of-speech tags and polarity scores. We have used two machine-learning algorithms to evaluate the performance of this resource on a sentiment analysis task. The lexicon is freely available and can be downloaded from our website.

With the advent of Internet, people actively express their opinions about products, services, events, political parties, etc., in social media, blogs, and website comments. The amount of research work on sentiment analysis is growing explosively. However, the majority of research efforts are devoted to English-language data, while a great share of information is available in other languages. We present a state-of-the-art review on multilingual sentiment analysis. More importantly, we compare our own implementation of existing approaches on common data. Precision observed in our experiments is typically lower than the one reported by the original authors, which we attribute to the lack of detail in the original presentation of those approaches. Thus, we compare the existing works by what they really offer to the reader, including whether they allow for accurate implementation and for reliable reproduction of the reported results.

International audience; Human Posture Classification(HPC) is used in many fields such as as human computer interfacing, security surveillance, rehabilitation, remote monitoring, and so on. This paper compares the performance of different classifiers in the detection of 3 postures, sitting, standing, and lying down, which was recorded using Microsoft Kinect cameras. The Machine Learning classifiers used included the Support Vector Classifier, Naive Bayes, Logistic Regression, K-Nearest Neighbours, and Random Forests. The Deep Learning ones included the standard Multi-Layer Perceptron, Convolutional Neural Networks(CNN), and Long Short Term Memory Networks(LSTM). It was observed that Deep Learning methods outperformed the former and that the one-dimensional CNN performed the best with an accuracy of 93.45%.

BackgroundGlobal efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general public. As such, it is crucial that governments and public health agencies understand public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions.ObjectiveThe aim of this study was to develop and apply an artificial intelligence–based approach to analyze public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the public attitude and concerns regarding COVID-19 vaccines.MethodsOver 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning–based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis.ResultsOverall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly.ConclusionsArtificial intelligence–enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing public attitude. Such analyses could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake.