37 research outputs found

    Analyzing Public Sentiment on COVID-19 Pandemic

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    Sentiment analysis is a method of understanding the user sentiment expressed in the form of text. Social media is the best place to capture the public\u27s opinion regarding how they feel about current events. The Corona Virus Disease-2019 (COVID-19) is one of the worst pandemics we have experienced so far. An important observation is that this pandemic has not only affected the public\u27s physical health but also took a toll on their mental health. Reddit is a social news discussion site where people discuss topics around current affairs in smaller groups called subreddits. The project\u27s primary focus is to build a deep learning model that can classify and help analyze user sentiments about Covid-19 on Reddit. The model has been built by evaluating the performance of different classifiers on the Twitter dataset, called Sentiment140. Experiments with varying feature combinations have been evaluated on deep learning models, including Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM). The idea is to build a model by combining the best of both architectures. LSTM excels at storing the forward information, whereas CNN can capture the local features. After reviewing these experiments, the best-performing model has been used to classify and analyze the sentiment of the Reddit users over different changes due to the Covid-19 pandemic. Overall, there have been some interesting changes in user reaction trends for posts related to Covid-19 under each subreddit over thirteen months, starting from Mar \u2720 to Mar \u2721

    1-D modelling and 3-D simulation of confined bubble formation and formation and pressure fluctuations during flow boiling in a microchannel with a rectangular cross-section of high aspect ratio

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    Copyright @ 2009 ASMEA simple 1-D model with low requirements for computing time is required to investigate parametric influences on the potentially adverse effects of pressure fluctuations driven by confined vapour bubble growth in microchannel evaporative cooling systems operating at high heat fluxes. A model is developed in this paper for the particular conditions of a channel of rectangular cross-section with high aspect ratio with a constant inlet flow rate (zero upstream compressibility). (The model will later be extended to the conditions of finite upstream compressibility that lead to transient flow reversal). Some parametric trends predicted by the model are presented. The simplifying assumptions in the model are examined in the light of a 3-D simulation by a commercial CFD code, described in an accompanying paper by the same authors. The predictions of pressure changes are in reasonable agreement. It is suggested that the 1-D model will be a useful design tool.This work is supported by the UK Engineering and Physical Sciences Research Council (EPSRC) under grants EP/D500095/1 and EP/D500125/1
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