Deep Learning Structure for Cross-Domain Sentiment Classification Based on Improved Cross Entropy and Weight

<div class="floats-full-page floats-full-page-other"><h2>Table 2</h2><div class="floats-full-page-caption"><div>Dataset table.</div></div><div class="floats-full-page-content"><table class="table-group" id="tab2"><tr><td><table class="table"><tr><td class="thead-hr" colspan="4"><hr /></td></tr><tr class="thead"><td class="align_left">Data</td><td class="align_center">Positive emotion sample</td><td class="align_center">Negative emotion sample</td><td class="align_center">Sum of samples</td></tr><tr><td class="thead-hr" colspan="4"><hr /></td></tr><tr><td class="align_left">English</td><td class="align_center">25000</td><td class="align_center">25000</td><td class="align_center">50000</td></tr><tr><td class="align_left">Chinese</td><td class="align_center">10679</td><td class="align_center">10428</td><td class="align_center">21107</td></tr><tr class="table-tr"><td colspan="4"><hr class="tbody-hr" /></td></tr></table></td></tr></table></div></div>

Table 2 | Deep Learning Structure for Cross-Domain Sentiment Classification Based on Improved Cross Entropy and Weight

title

Table 2: Deep Learning Structure for Cross-Domain Sentiment Classification Based on Improved Cross Entropy and Weight

description

Table 2

About this journal

<p><em>Scientific Programming </em>provides a forum for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.</p>


Journal profile

Meet the editorial board

<p>Chief Editor, Professor Tramontana, is based at the University of Catania and his research primarily concerns the areas of software engineering and distributed systems.</p>


Editor spotlight

Submit to Special Issue

We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.