01553nas a2200265   4500000000100000000000100001008004100002260000900043653003600052653002800088653002300116653003200139653000800171653003300179653002300212100001800235700001400253700001900267700001900286700001500305245008700320300001200407490000700419520086100426       2020                            d  c202010aaspect-based sentiment analysis10aco-reference resolution10adependency parsing10anatural language processing10aNLP10arule-base sentiment analysis10asentiment analysis1 aIvana Mestric1 aArvid Kok1 aGiavid Valiyev1 aMichael Street1 aPeter Lenk00aAspect Level Sentiment Analysis Methods Applied to Text in Formal Military Reports  a227-2380 v463 a<p>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.</p>