{"id":9591,"date":"2022-06-21T05:59:19","date_gmt":"2022-06-21T05:59:19","guid":{"rendered":"https:\/\/support.divominer.com\/en\/?post_type=ht_kb&#038;p=9591"},"modified":"2024-04-11T02:42:54","modified_gmt":"2024-04-11T02:42:54","slug":"user-manual-for-%e3%80%90algorithm-mining%e3%80%91","status":"publish","type":"ht_kb","link":"https:\/\/support.divominer.com\/en\/knowledge-base\/user-manual-for-%e3%80%90algorithm-mining%e3%80%91\/","title":{"rendered":"How to use [Algorithm &#038; mining]"},"content":{"rendered":"\n<p>[Algorithm &amp; Mining] offers a variety of algorithms such as sentiment analysis (positive\/negative), sentiment analysis (emotion classification), K-Means clustering, topic model (LDA), similarity analysis, semantic network analysis and social network analysis. <\/p>\n\n\n\n<p>Creating algorithmic models consumes computing resources. You can receive 500 free points of resources every month (click on the orange &#8220;Free Resources&#8221; button in the lower right corner) for testing and exploring algorithmic results. If you run out of resources, you can always click [Purchase Resources].<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" width=\"1024\" height=\"505\" src=\"https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/7-1024x505.png\" alt=\"\" class=\"wp-image-9668\" srcset=\"https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/7-1024x505.png 1024w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/7-300x148.png 300w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/7-768x379.png 768w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/7-50x25.png 50w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/7-1536x757.png 1536w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/7-920x454.png 920w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/7-600x296.png 600w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/7-320x158.png 320w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/7.png 1600w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>After clicking on [Create an Algorithm Task], select the algorithm model you want to create. Follow the instructions to create the algorithm, execute it, and then view the corresponding data results.<\/p>\n\n\n\n<p>Each algorithm module comes with a detailed introduction document. You can click on [Detailed Introduction] on the algorithm module page to view it.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" width=\"1024\" height=\"505\" src=\"https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/8-1-1024x505.png\" alt=\"\" class=\"wp-image-9669\" srcset=\"https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/8-1-1024x505.png 1024w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/8-1-300x148.png 300w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/8-1-768x379.png 768w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/8-1-50x25.png 50w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/8-1-1536x757.png 1536w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/8-1-920x454.png 920w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/8-1-600x296.png 600w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/8-1-320x158.png 320w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2022\/06\/8-1.png 1600w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>Following the execution of certain algorithms, the outcomes can be stored in designated fields (variables) for later mixed analysis with other variables (conduct mixed analysis in [Basic Statistics]). The algorithm models capable of generating and preserving computation results are: sentiment analysis (positive\/negative), sentiment analysis (emotion classification), K-Means clustering, topic model (LDA), and similarity analysis.<\/p>\n","protected":false},"author":3,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0},"ht_kb_category":[5],"ht_kb_tag":[31,16],"_links":{"self":[{"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/ht_kb\/9591"}],"collection":[{"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/ht_kb"}],"about":[{"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/types\/ht_kb"}],"author":[{"embeddable":true,"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/comments?post=9591"}],"version-history":[{"count":11,"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/ht_kb\/9591\/revisions"}],"predecessor-version":[{"id":10611,"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/ht_kb\/9591\/revisions\/10611"}],"wp:attachment":[{"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/media?parent=9591"}],"wp:term":[{"taxonomy":"ht_kb_category","embeddable":true,"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/ht_kb_category?post=9591"},{"taxonomy":"ht_kb_tag","embeddable":true,"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/ht_kb_tag?post=9591"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}