{"id":305,"date":"2018-04-11T13:19:25","date_gmt":"2018-04-11T19:19:25","guid":{"rendered":"http:\/\/briancroxall.net\/w18dh\/?p=305"},"modified":"2018-04-16T14:17:05","modified_gmt":"2018-04-16T20:17:05","slug":"topic-model","status":"publish","type":"post","link":"https:\/\/briancroxall.net\/w18dh\/uncategorized\/topic-model\/","title":{"rendered":"Duffy Topic Model"},"content":{"rendered":"

Topic Models Created<\/h3>\n
    \n
  1. Cleaned-simple data, 10 topics, stopwords included, 1000 iterations, no optimization<\/span><\/li>\n
  2. Cleaned-simple data, 20 topics, stopwords included, 1000 iterations, no optimization<\/span><\/li>\n
  3. Cleaned-simple data, 15 topics, stopwords included, 1000 iterations, no optimization<\/span><\/li>\n
  4. No stopwords data, 10 topics, no stopwords, 1000 iterations, no optimization<\/span><\/li>\n
  5. No stopwords data, 20 topics, no stopwords, 1000 iterations, no optimization<\/span><\/li>\n
  6. No stopwords data, 20 topics, no stopwords, 9000 iterations, 900 optimization interval<\/span><\/li>\n
  7. Cleaned-simple data,<\/span> 15 topics, stopwords included, 9000 iterations, 900 optimization interval<\/span><\/li>\n<\/ol>\n

    We chose to use the 7th topic model we created. We felt that 15 was a good number of topics and that the inclusion of the stop words added a clarity to the results. Inclusion of stop words guarantees that no data is lost. We also chose to use 9000 iterations in order to refine the topics best as possible.<\/span><\/p>\n

    Semantically Evident and Opaque Topics<\/h3>\n

    This topic model has both semantically evident and semantically opaque topics. Aside from the \u201cEnglish language\u201d topic, which groups together many of the stop words, one semantically evident topic, which we named \u201cnature,\u201d stood out. It is listed as follows: <\/span><\/p>\n

    bird sea big weasel egg shape fish bite gate bang rose except sue great petals fly suck cross grass slurp<\/b><\/p>\n

    This topic was easy to identify because of the amount of words it contained that related to the outdoors and animals. The topic not only lists animals, but actions that are performed by animals, such as \u201cbite\u201d and \u201cfly\u201d as well as other related words like \u201cegg.\u201d In addition, nature words like sea and petals show the surrounding in which these animals are found. <\/span><\/p>\n

    Another semantically evident topic is one that we labeled \u201cthe sea.\u201d The words in this topic model are listed here: <\/span><\/p>\n

    his beneath body upon above moll seas somewhere hair start dolphins mention perfect weather may pearl darling sick tremble further<\/b><\/p>\n

    This topic was not as easily identifiable as the \u201cnature\u201d topic, because there are more words in this topic that seem to not be directly related, but there was a strong enough theme revolving around \u201csea\u201d words that it seemed like the most prominent topic in this group. Words such as \u201cseas,\u201d \u201cdolphins,\u201d \u201cweather,\u201d and \u201cpearl\u201d clearly relate to the sea and physical words such as \u201cbody,\u201d \u201chair,\u201d and \u201ctremble\u201d may point toward poems that explore human interactions with the sea.<\/span><\/p>\n

    Topic 2 was a semantically opaque topic. While some pairs of words seem to be related to each other, there was not an apparent theme among the whole set. <\/span><\/p>\n

    say miss does give unless church says bread green brought perhaps has pink maiden soil something help been possible living<\/b><\/p>\n

    Some words in this topic group denote religion, including \u201cchurch\u201d and \u201cbread,\u201d some could relate to nature, including \u201cgreen\u201d and \u201csoil,\u201d but there doesn\u2019t seem to be much of a theme beyond these words that ties the whole group together.<\/span><\/p>\n

    Another semantically opaque topic from this model is topic 6. <\/span><\/p>\n

    its woke river bowl dusk sit glass spoon thin wall apple pressing clothes looking strangers moments gently dawn perfect prayer<\/b><\/p>\n

    This topic group contains words that talk about food, place, nature, religion, and personal interaction. There is not one clear topic that ties these words together, but it is easy to understand why some of the words would be used together, such as, \u201cpressing\u201d and \u201cclothes\u201d and \u201cbowl\u201d and \u201cspoon.\u201d<\/span><\/p>\n

    Visualization<\/h3>\n

    \"\"<\/a> \"\"<\/a> \"\"<\/a><\/p>\n

    Observations<\/h3>\n

    After spending weeks analyzing our poetry by hand, topic modeling gave us a new perspective on the different perspective on the themes and language present throughout Duffy\u2019s poetry. There were a number of surprises as well as expected outcomes in the topic modeling. For example, in topic 3, which we dubbed as the \u201cmale\u201d topic, the first ten poems are from <\/span>The World\u2019s Wife<\/span><\/i>. Upon reading the book, it is often clearly about male\/female relationships. <\/span>The World\u2019s Wife<\/span><\/i> also is noticeably absent in the first thirty slots of the topic \u201cEnglish language.\u201d Because the poems in this book of poetry constructed in a more narrative style, it makes sense that there would be fewer words that deal directly with language. In the other books of poetry, the actual meanings are built off the use and precision of otherwise very \u201cnormal\u201d words.<\/span><\/p>\n

    There were some surprises that came from the topic modeling. We labelled one topic (number 4) as a topic totally about \u201cMrs Beast\u201d from <\/span>The World\u2019s Wife<\/span><\/i>. However, when we looked at the compositions of the individual poems, \u201cMrs Beast\u201d was actually second. \u201cPoker in the Falklands with Henry and Jim\u201d from <\/span>Standing Female Nude<\/span><\/i> came in first. While both poems deal with poker, we hadn\u2019t previously recognized a relationship between the two poems until seeing them side by side in the same topic. Another surprise was that our topic titled \u201cthe other\u201d didn\u2019t see any poems from <\/span>The World\u2019s Wife<\/span><\/i> until the sixtieth spot. While it\u2019s a theme we had identified by hand as we analyzed the poetry, the topic model returned different conclusions. This example, as well as the other examples mentioned, demonstrates how both human and machine learning can help to discover meaning where the other might miss it. Doing the topic modeling has helped us especially find many cases where <\/span>The World\u2019s Wife<\/span><\/i> is noticeably different from Duffy\u2019s other books of poetry. <\/span><\/p>\n

    Created by Chris Dichmann, Maddie Calder, McKinsey Koch, and Lorin Groesbeck<\/p>\n","protected":false},"excerpt":{"rendered":"

    Topic Models Created Cleaned-simple data, 10 topics, stopwords included, 1000 iterations, no optimization Cleaned-simple data, 20 topics, stopwords included, 1000 iterations, no optimization Cleaned-simple data, 15 topics, stopwords included, 1000 iterations, no optimization No stopwords data, 10 topics, no stopwords, 1000 iterations, no optimization No stopwords data, 20 topics, no stopwords, 1000 iterations, no optimization […]<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[1],"tags":[],"class_list":["post-305","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/briancroxall.net\/w18dh\/wp-json\/wp\/v2\/posts\/305","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/briancroxall.net\/w18dh\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/briancroxall.net\/w18dh\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/briancroxall.net\/w18dh\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/briancroxall.net\/w18dh\/wp-json\/wp\/v2\/comments?post=305"}],"version-history":[{"count":4,"href":"https:\/\/briancroxall.net\/w18dh\/wp-json\/wp\/v2\/posts\/305\/revisions"}],"predecessor-version":[{"id":315,"href":"https:\/\/briancroxall.net\/w18dh\/wp-json\/wp\/v2\/posts\/305\/revisions\/315"}],"wp:attachment":[{"href":"https:\/\/briancroxall.net\/w18dh\/wp-json\/wp\/v2\/media?parent=305"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/briancroxall.net\/w18dh\/wp-json\/wp\/v2\/categories?post=305"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/briancroxall.net\/w18dh\/wp-json\/wp\/v2\/tags?post=305"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}