دانلود و نمایش مقالات مرتبط با Social media analysis::صفحه 1
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نتیجه جستجو - Social media analysis

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 Towards a real-time processing framework based on improved distributed recurrent neural network variants with fastText for social big data analytics
به سمت یک چارچوب پردازش در زمان واقعی بر اساس بهبود انواع شبکه عصبی مکرر توزیع شده با fastText برای تجزیه و تحلیل داده های بزرگ اجتماعی-2020
Big data generated by social media stands for a valuable source of information, which offers an excellent opportunity to mine valuable insights. Particularly, User-generated contents such as reviews, recommendations, and users’ behavior data are useful for supporting several marketing activities of many companies. Knowing what users are saying about the products they bought or the services they used through reviews in social media represents a key factor for making decisions. Sentiment analysis is one of the fundamental tasks in Natural Language Processing. Although deep learning for sentiment analysis has achieved great success and allowed several firms to analyze and extract relevant information from their textual data, but as the volume of data grows, a model that runs in a traditional environment cannot be effective, which implies the importance of efficient distributed deep learning models for social Big Data analytics. Besides, it is known that social media analysis is a complex process, which involves a set of complex tasks. Therefore, it is important to address the challenges and issues of social big data analytics and enhance the performance of deep learning techniques in terms of classification accuracy to obtain better decisions. In this paper, we propose an approach for sentiment analysis, which is devoted to adopting fastText with Recurrent neural network variants to represent textual data efficiently. Then, it employs the new representations to perform the classification task. Its main objective is to enhance the performance of well-known Recurrent Neural Network (RNN) variants in terms of classification accuracy and handle large scale data. In addition, we propose a distributed intelligent system for real-time social big data analytics. It is designed to ingest, store, process, index, and visualize the huge amount of information in real-time. The proposed system adopts distributed machine learning with our proposed method for enhancing decision-making processes. Extensive experiments conducted on two benchmark data sets demonstrate that our proposal for sentiment analysis outperforms well-known distributed recurrent neural network variants (i.e., Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)). Specifically, we tested the efficiency of our approach using the three different deep learning models. The results show that our proposed approach is able to enhance the performance of the three models. The current work can provide several benefits for researchers and practitioners who want to collect, handle, analyze and visualize several sources of information in real-time. Also, it can contribute to a better understanding of public opinion and user behaviors using our proposed system with the improved variants of the most powerful distributed deep learning and machine learning algorithms. Furthermore, it is able to increase the classification accuracy of several existing works based on RNN models for sentiment analysis.
Keywords: Big data | FastText | Recurrent neural networks | LSTM | BiLSTM | GRU | Natural language processing | Sentiment analysis | Social big data analytics
مقاله انگلیسی
2 Determining disaster severity through social media analysis: Testing the methodology with South East Queensland Flood tweets
تعیین شدت فاجعه از طریق تحلیل رسانه های اجتماعی: آزمایش روش با توییت سیل جنوب شرقی کوئینزلند-2020
Social media was underutilised in disaster management practices, as it was not seen as a real-time ground level information harvesting tool during a disaster. In recent years, with the increasing popularity and use of social media, people have started to express their views, experiences, images, and video evidences through different social media platforms. Consequently, harnessing such crowdsourced information has become an opportunity for authorities to obtain enhanced situation awareness data for efficient disaster management practices. Nonetheless, the current disaster-related Twitter analytics methods are not versatile enough to define disaster impacts levels as interpreted by the local communities. This paper contributes to the existing knowledge by applying and extending a well-established data analysis framework, and identifying highly impacted disaster areas as perceived by the local communities. For this, the study used real-time Twitter data posted during the 2010–2011 South East Queensland Floods. The findings reveal that: (a) Utilising Twitter is a promising approach to reflect citizen knowledge; (b) Tweets could be used to identify the fluctuations of disaster severity over time; (c) The spatial analysis of tweets validates the applicability of geo-located messages to demarcate highly impacted disaster zones.
Keywords: Social media | Data analytics | Big data | Crowdsourcing | Volunteered geographic information | South East Queensland Floods
مقاله انگلیسی
3 تشخیص رویداد بر روی رسانه‌های اجتماعی بزرگ با استفاده از تحلیل زمانی
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 20
شبکه‌های رسانه‌های اجتماعی اکنون به‌عنوان یکی از کانال‌های خبری اصلی در نظر گرفته می‌شوند که خبرهایی که سایر رسانه‌ها آنها را منعکس می‌کنند، زودتر ارائه می‌دهند. به‌دلیل محبوبیت بسیار زیاد رسانه‌های اجتماعی، اخیرا توجه‌ی پژوهشگران به مسئله‌ی تشخیص رویداد مبتنی بر رسانه‌های اجتماعی جلب شده است. رویکردهای موجود بر روی ویژگی‌های متمرکز شده‌اند که منعکس‌کننده‌ی همه‌ی ویژگی‌های شبکه‌های اجتماعی نمی‌باشند. برای هدف این تحقیق، ما یک رویداد را به‌عنوان یک رخداد که دارای نیرو و تکانه‌ی کافی می‌باشد، تعریف می‌کنیم که می‌تواند تغییر قابل‌توجهی در زمینه‌ی شبکه‌ی اجتماعی به‌وجود آورد. اینگونه تعریف برای ما یک چشم‌انداز گسترده را فراهم می‌کند که ما می‌توانیم تصویر بزرگی از شبکه‌های اجتماعی را مشاهده نماییم. ما در این تحقیق یک چارچوب جدید را برای تشخیص دادن رویدادها بر روی رسانه‌های اجتماعی ارائه می‌دهیم. ما یک روش زمانی را جهت تشخیص تغییر ساختاری شبکه‌های اجتماعی معرفی می‌کنیم که منعکس‌کننده‌ی رخدادهای یک رویداد با استفاده از الگوریتم‌های فراگیری ماشین می‌باشد. ما در این پژوهش نشان می‌دهیم که پردازش کردن شبکه‌های اجتماعی زمانی، سازگاری کامل شبکه‌ی اجتماعی را ثبت می‌کند که منجر به ایجاد دقت بالاتری از تشخیص رویداد می‌شود.
کلمات کلیدی: داده‌های بزرگ | داده کاوی | تحلیل رسانه‌های اجتماعی | تشخیص رویداد | یادگیری ماشین.
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