<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ivana Ilic Mestric</style></author><author><style face="normal" font="default" size="100%">Arvid Kok</style></author><author><style face="normal" font="default" size="100%">Giavid Valiyev</style></author><author><style face="normal" font="default" size="100%">Michael Street</style></author><author><style face="normal" font="default" size="100%">Peter Lenk</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aspect Level Sentiment Analysis Methods Applied to Text in Formal Military Reports</style></title><secondary-title><style face="normal" font="default" size="100%">Information &amp; Security: An International Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">aspect-based sentiment analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">co-reference resolution</style></keyword><keyword><style  face="normal" font="default" size="100%">dependency parsing</style></keyword><keyword><style  face="normal" font="default" size="100%">natural language processing</style></keyword><keyword><style  face="normal" font="default" size="100%">NLP</style></keyword><keyword><style  face="normal" font="default" size="100%">rule-base sentiment analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">sentiment analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">46</style></volume><pages><style face="normal" font="default" size="100%">227-238</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Many military functions such as intelligence collection or lessons learned analysis demand an understanding of situations derived from large quantities of written material. This paper describes approaches to gain greater understanding of document content by applying rule-based approaches in addition to open source machine learning models. The performance of two approaches to sentiment analysis are assessed, when operating on document sets from NATO sources. This combination enables analysts to identify items of interest within large document sets more effectively, by indicating the sentiment around specific aspects (nouns) which refer to a specific target (noun) in the text. This enables data science to give users a more detailed understanding of the content of large quantities of documents with respect to a particular target or subject.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><section><style face="normal" font="default" size="100%">227</style></section></record></records></xml>