4 research outputs found
Evaluation of soil physical quality in dominant series of calcareous soils in south-west of Iran
Calcareous soils are widely spread in arid and semiarid regions. Carbonates can affect soil quality by influencing soil pH, structure and soil available water. There are lots of calcareous soils in Iran and especially Khuzestan province, so, providing sustainable agriculture evaluating the soil quality is essential. This study was done to evaluate the soil physical quality in dominant calcareous soil series in Khuzestan province, Iran. Soil physical quality indicators, including Dexter's S index, air capacity, soil available water capacity, relative water capacity and macroporosity were calculated. The results showed that, based on Dexter's S index, only one calcareous soil series had a poor physical quality (S < 0.035). However, the simultaneous evaluation of different soil quality indicators showed that 56 % and 22 % of studied calcareous soil series had limited aeration and soil available water, respectively. While the weakest soil physical quality was related to the southeastern soil of Ahvaz, with both aeration and soil available water limitations. The results showed that the proper assessment of soil physical quality in calcareous soils requires considering more physical indicators than just Dexter's S index related to soil aeration condition including air capacity and macroporosity
BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets
The free flow of information has been accelerated by the rapid development of
social media technology. There has been a significant social and psychological
impact on the population due to the outbreak of Coronavirus disease (COVID-19).
The COVID-19 pandemic is one of the current events being discussed on social
media platforms. In order to safeguard societies from this pandemic, studying
people's emotions on social media is crucial. As a result of their particular
characteristics, sentiment analysis of texts like tweets remains challenging.
Sentiment analysis is a powerful text analysis tool. It automatically detects
and analyzes opinions and emotions from unstructured data. Texts from a wide
range of sources are examined by a sentiment analysis tool, which extracts
meaning from them, including emails, surveys, reviews, social media posts, and
web articles. To evaluate sentiments, natural language processing (NLP) and
machine learning techniques are used, which assign weights to entities, topics,
themes, and categories in sentences or phrases. Machine learning tools learn
how to detect sentiment without human intervention by examining examples of
emotions in text. In a pandemic situation, analyzing social media texts to
uncover sentimental trends can be very helpful in gaining a better
understanding of society's needs and predicting future trends. We intend to
study society's perception of the COVID-19 pandemic through social media using
state-of-the-art BERT and Deep CNN models. The superiority of BERT models over
other deep models in sentiment analysis is evident and can be concluded from
the comparison of the various research studies mentioned in this article.Comment: 20 pages, 5 figure
Identifying Influentials in Social Networks
In recent years, social networks have become very popular and an integral part of everyday life. People express their feelings and experiences in this virtual environment and become aware of others’ opinions and interests. Among them, influential users play an important role in disseminating information on social networks. Identifying such influencers is important in designing techniques to increase the speed of information dissemination. Such techniques are applicable in various fields including viral marketing, preventing the dissemination of harmful information, providing specialized recommendations, etc. Various approaches have been used to detect influencers on social networks, mostly based on the individual’s position in the network structure and their interactions. Considering the strengths and weaknesses of the previous methods, this study presents a novel method based on the content of the users’ posts without considering the network structure. This is done using a combination of high-level features extracted from images to measure the individual’s influence. Users’ images are investigated from three aspects: (1) color scheme, (2) advertising nature, (3) images’ semantics. To describe each of these aspects, feature extraction methods were used with acceptable accuracy in recognizing influential users. Finally, to achieve greater efficiency and precision, feature-combination methods have been investigated to provide an integrated classifier