{"id":3039,"date":"2019-01-29T03:29:25","date_gmt":"2019-01-29T03:29:25","guid":{"rendered":"https:\/\/iris.siue.edu\/technology-literature\/?p=3039"},"modified":"2019-01-29T03:29:25","modified_gmt":"2019-01-29T03:29:25","slug":"blog-2-voyant","status":"publish","type":"post","link":"https:\/\/iris.siue.edu\/technology-literature\/2019\/01\/29\/blog-2-voyant\/","title":{"rendered":"Blog 2 – Voyant"},"content":{"rendered":"

In beginning my tinkering with on Voyant, I entered the full text of Herland <\/em>in the search, as entering the URL of the e-text did not at all give accurate results regarding the contents of the text. Once being navigated to the Corpus, it became clear that the novel at least<\/em> has something to do with women and men named Terry and Jeff, as these were among the most frequently used words. The word \u201ccountry\u201d also popped up quite a bit (137 times, compared to \u201cJeff\u201d being mentioned 152 times); this caused me to assume the women and these men had something to do with a country. Looking back after reading the first few chapters, it\u2019s safe to say that is correct.<\/p>\n

After exploring the tools Voyant has to offer, Correlation, Bubblelines, and WordTree are among my favorite features to use. To further investigate the significance of the most popular words in the book, I decided to narrow my search to \u201cTerry\u201d and \u201cwomen\u201d, as the Cirrus and Summary tools revealed those to be the two most frequently used words at 247 and 209 words, respectively. Beginning with Correlation, I found that \u201cwomen\u201d was most highly correlated with \u201catmosphere\u201d, \u201chorses\u201d, \u201cspeaking\u201d, \u201ctutor\u201d, \u201ccattle\u201d, and \u201cwalled\u201d. With \u201cTerry\u201d, \u201ccan\u2019t\u201d, \u201ckill\u201d, \u201cadvanced\u201d, \u201cclimbed\u201d, and \u201ccolored\u201d were among the most highly correlated. Interestingly enough, the highly correlated words for both \u201cTerry\u201d and \u201cwomen\u201d seemed to perfectly match up with the frequency of use per section seen in the Trends tool. Moving on to the Bubblelines tool, which appears to be a similar tool to Trends, it is apparent that \u201cTerry\u201d appears consistently up until around \u00be of the way through the novel, in which it then suddenly drops off and is barely used. This begs the question, \u201cWhat happened to Terry?\u201d Around this same time, \u201cwomen\u201d is used less, but is still used more consistently. The last tool I found quite useful is WordTree, which is exactly what the name suggests \u2013 a word tree. I typed in \u201cwomen\u201d, and the results were quite interesting and different from what I saw in previous tools used. The words connected with women were \u201ccareful\u201d, \u201cmultitude\u201d, and \u201cparthenogenic\u201d. This, to me, suggests a somewhat sinister, perplexing twist to the novel.<\/p>\n

Although Voyant is a helpful tool in trying to figure out what are the crucial terms and ties between those terms in a novel, for a novice at the program such as me, that is all it is \u2013 trying<\/em>. I suppose, in a way, it is similar to asking someone for a quick synopsis of the book in passing. Another important point, though, is that this program seems to be more statistical and technical than it is focused on the true literary analysis of the text, something I am more acquainted with. With that said, it would be more user-friendly if Voyant had an easier tutorial or had an \u201cAbout\u201d tab when you put your mouse overtop a tool, such as Bubblelines; that way, the user knows for sure exactly<\/em> what they are analyzing as well as how to make better use of it. Overall, though, Voyant is an exceptional tool for quickly analyzing a text\u2019s most important terms, aiding in deciphering a text before even reading it.<\/p>\n","protected":false},"excerpt":{"rendered":"

In beginning my tinkering with on Voyant, I entered the full text of Herland in the search, as entering the URL of the e-text did not at all give accurate results regarding the contents of the text. Once being navigated to the Corpus, it became clear that the novel at least has something to do with women and men named Terry and Jeff, as these were among the most frequently used words. The word \u201ccountry\u201d also popped up quite a bit (137 times, compared to \u201cJeff\u201d being mentioned 152 times); this caused me to assume the women and these men had something to do with a country. Looking back after reading the first few chapters, it\u2019s safe to say that is correct. After exploring the tools Voyant has to offer, Correlation, Bubblelines, and WordTree are among my favorite features to use. To further investigate the significance of the most popular words in the book, I decided to narrow my search to \u201cTerry\u201d and \u201cwomen\u201d, as the Cirrus and Summary tools revealed those to be the two most frequently used words at 247 and 209 words, respectively. Beginning with Correlation, I found that \u201cwomen\u201d was most highly correlated with \u201catmosphere\u201d, \u201chorses\u201d, \u201cspeaking\u201d, \u201ctutor\u201d, \u201ccattle\u201d, and \u201cwalled\u201d. With \u201cTerry\u201d, \u201ccan\u2019t\u201d, \u201ckill\u201d, \u201cadvanced\u201d, \u201cclimbed\u201d, and \u201ccolored\u201d were among the most highly correlated. Interestingly enough, the highly correlated words for both \u201cTerry\u201d and \u201cwomen\u201d seemed to perfectly match up with the frequency of use per section seen in the Trends tool. Moving on to the Bubblelines tool, which appears to be a similar tool to Trends, it is apparent that \u201cTerry\u201d appears consistently up until around \u00be of the way through the novel, in which it then suddenly drops off and is barely used. This begs the question, \u201cWhat happened to Terry?\u201d Around this same time, \u201cwomen\u201d is used less, but is still used more consistently. The last tool I found quite useful is WordTree, which is exactly what the name suggests \u2013 a word tree. I typed in \u201cwomen\u201d, and the results were quite interesting and different from what I saw in previous tools used. The words connected with women were \u201ccareful\u201d, \u201cmultitude\u201d, and \u201cparthenogenic\u201d. This, to me, suggests a somewhat sinister, perplexing twist to the novel. Although Voyant is a helpful tool in trying to figure out what are the crucial terms and ties between those terms in a novel, for a novice at the program such as me, that is all it is \u2013 trying. I suppose, in a way, it is similar to asking someone for a quick synopsis of the book in passing. Another important point, though, is that this program seems to be more statistical and technical than it is focused on the true literary analysis of the text, something I am more acquainted with. With that said, it would be more user-friendly if Voyant had an easier tutorial or had an \u201cAbout\u201d tab when you put your mouse overtop a tool, such as Bubblelines; that way, the user knows for sure exactly what they are analyzing as well as how to make better use of it. Overall, though, Voyant is an exceptional tool for quickly analyzing a text\u2019s most important terms, aiding in deciphering a text before even reading it.<\/p>\n","protected":false},"author":37,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_sb_is_suggestion_mode":false,"_sb_show_suggestion_boards":false,"_sb_show_comment_boards":false,"_sb_suggestion_history":"","_sb_update_block_changes":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-3039","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/iris.siue.edu\/technology-literature\/wp-json\/wp\/v2\/posts\/3039","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/iris.siue.edu\/technology-literature\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/iris.siue.edu\/technology-literature\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/iris.siue.edu\/technology-literature\/wp-json\/wp\/v2\/users\/37"}],"replies":[{"embeddable":true,"href":"https:\/\/iris.siue.edu\/technology-literature\/wp-json\/wp\/v2\/comments?post=3039"}],"version-history":[{"count":1,"href":"https:\/\/iris.siue.edu\/technology-literature\/wp-json\/wp\/v2\/posts\/3039\/revisions"}],"predecessor-version":[{"id":3040,"href":"https:\/\/iris.siue.edu\/technology-literature\/wp-json\/wp\/v2\/posts\/3039\/revisions\/3040"}],"wp:attachment":[{"href":"https:\/\/iris.siue.edu\/technology-literature\/wp-json\/wp\/v2\/media?parent=3039"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/iris.siue.edu\/technology-literature\/wp-json\/wp\/v2\/categories?post=3039"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/iris.siue.edu\/technology-literature\/wp-json\/wp\/v2\/tags?post=3039"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}