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Product Sentiment Analysis for Amazon Reviews

Arwa S. M. AlQahtani
<span title="2021-07-14">2021</span> <i title="Zenodo"> Zenodo </i> &nbsp;
The Experiment was Conducted on Multiclass Classifications, Then we Selected the Best Performing Model And Re-Trained It on the Binary Classification.  ...  This Research Provides an Analysis of the Amazon Reviews Dataset and Studies Sentiment Classification with Different Machine Learning Approaches.  ...  Further study used a semantic approach; the authors of [9] proposed sentiment classification based on word2vec and SVMperf. The study consists of two parts.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.5100054">doi:10.5281/zenodo.5100054</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5bxaueg4vffyvn6foo5ukugx5y">fatcat:5bxaueg4vffyvn6foo5ukugx5y</a> </span>
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Sentiment Analysis of Danmaku Videos Based on Naïve Bayes and Sentiment Dictionary

Zhi Li, Rui Li
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
These live comments contain complex and rich sentiments, reflecting users' instant opinions and feelings on video programs.  ...  Danmaku is a live commenting function where the comments related to the video being screened are created by users and prominently shown in real-time on the video screen.  ...  [24] proposed a method based on Word2vec and SVMperf to classify Chinese comment texts.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2986582">doi:10.1109/access.2020.2986582</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uq2sffryvvgfpkzbcmoqmgsbhu">fatcat:uq2sffryvvgfpkzbcmoqmgsbhu</a> </span>
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Kelime Vektörü Yöntemlerinin Model Oluşturma Sürelerinin Karşılaştırılması

METİN BİLGİN
<span title="2019-04-30">2019</span> <i title="International Journal of Informatics Technologies"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/heqikokm35ervdsppquidtxm7i" style="color: black;">Bilişim Teknolojileri Dergisi</a> </i> &nbsp;
In this study, three different methods for modeling a text are suggested on both CBoW and Skip-Gram. Its modeling time (training time) is measured.  ...  While the model is creating that has used two different methods CBoW and Skip-Gram of Word2Vec. Generally, the arithmetic mean is used for modeling a text with Word2Vec.  ...  Xu, "Chinese comments sentiment classification based on word2vec and SVMperf", Expert Systems with Applications, 42(4), 1857-1863, 2015. [2] B. Dickinson, W.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17671/gazibtd.472226">doi:10.17671/gazibtd.472226</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yjb5dofkt5e5zp4r6yqrjpsihe">fatcat:yjb5dofkt5e5zp4r6yqrjpsihe</a> </span>
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An LSTM&Topic-CNN Model for Classification of Online Chinese Medical Questions

Song Mao, Lu-lu Zhang, Zhen-guo Guan
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
The authors would like to thank the editor and the referees for their insightful comments that have led to a substantial improvement to an earlier version of the paper.  ...  [17] adopted a new method which combined the Word2vec and SVMperf to train and classify the Chinese comment texts. Edara et al.  ...  Hence, we establish an LSTM&Topic-CNN model based on the fusion of text semantic features and topic features and apply it to the classification of Chinese medical text.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3070375">doi:10.1109/access.2021.3070375</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wj5jiiwovrgule6vv7qqhtuvha">fatcat:wj5jiiwovrgule6vv7qqhtuvha</a> </span>
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A Two-Step Approach for Improving Sentiment Classification Accuracy

Muhammad Azam, Tanvir Ahmed, Rehan Ahmad, Ateeq Ur Rehman, Fahad Sabah, Rao Muhammad Asif
<span title="">2021</span> <i title="Computers, Materials and Continua (Tech Science Press)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/a6m5ovtnq5h6bb2dcb357moo4a" style="color: black;">Intelligent Automation and Soft Computing</a> </i> &nbsp;
The way toward investigating different opinions and gathering them in every one of these categories is known as Sentiment Analysis.  ...  The proposed article is an effort to find a model which can improve the classification accuracy of sentiment data.  ...  C4.5 88.5% [24] • Chinese comments on clothing • word2vec and SVMperf 87.1% to products 90.30% Table 2 : 2 Accuracy of base classifiers on the Amazon dataset Classifier 10,000 instances 3,000 instances  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.32604/iasc.2021.019101">doi:10.32604/iasc.2021.019101</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nrc2kuhsbjflfb4uketopq2r6u">fatcat:nrc2kuhsbjflfb4uketopq2r6u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220228132216/https://www.techscience.com/ueditor/files/iasc/TSP_IASC-30-3/TSP_IASC_19101/TSP_IASC_19101.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/91/a3/91a355dc2a319556a18feda89076e51cb1785e57.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.32604/iasc.2021.019101"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>