I found the piece \u201cSentiment Analysis and Subjectivity\u201d by Bing Lui interesting as it really expanded on idea of computational text analysis as a means to analyze data. When we first began to look into text mining in class, I was a little confused as to how computers were able to take something like a news article or opinion piece, something so subjective and in the \u201cgrey area\u201d, and turn it into something so black and white without going through the human thought-process of synthesizing the information. Lui\u2019s writing helped me to understand this as he talked about how exactly computers synthesize the information for \u201copinion mining\u201d.<\/span><\/p>\n

Opinion mining is done by looking at each individual sentence and wording within that sentence with certain subjectivity classifications that help us deem what the overall consensus of the written piece entails. After learning how the actual process works, I find it curious to compare \u201cSentiment Analysis and Subjectivity\u201d with what a peer of mine (BM) said in their post on \u201cText Mining\/Language Standardization\u201d, that a \u201ccomputer will never understand the emotional values and ever-changing expressions of human beings\u201d. From what I read today I think that computers are getting incredibly close to understanding the emotional values of human beings due to the work that has been done with opinion mining and the call for a better way to synthesize the public’s opinion and perception of things and products on a larger scale. Although there is a long way still to go, I think that this significant headway will be the basis for future breakthroughs. <\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

I found the piece “Sentiment Analysis and Subjectivity” by Bing Lui interesting as it really expanded on idea of computational text analysis as a means to analyze data. When we first began to look into text mining in class, I was a little confused as to how computers were able to take something like a…<\/p>\n","protected":false},"author":179,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-738","post","type-post","status-publish","format-standard","hentry","category-class"],"_links":{"self":[{"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/posts\/738","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/users\/179"}],"replies":[{"embeddable":true,"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/comments?post=738"}],"version-history":[{"count":2,"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/posts\/738\/revisions"}],"predecessor-version":[{"id":1301,"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/posts\/738\/revisions\/1301"}],"wp:attachment":[{"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/media?parent=738"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/categories?post=738"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/tags?post=738"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}