{"id":10854,"date":"2019-04-23T15:40:36","date_gmt":"2019-04-23T18:40:36","guid":{"rendered":"https:\/\/tarciziosilva.com.br\/blog\/?p=10854"},"modified":"2019-04-29T23:37:52","modified_gmt":"2019-04-30T02:37:52","slug":"text-analysis-with-antconc-for-social-media-data-exploration-intro-files-and-settings","status":"publish","type":"post","link":"https:\/\/tarciziosilva.com.br\/blog\/text-analysis-with-antconc-for-social-media-data-exploration-intro-files-and-settings\/","title":{"rendered":"Text Analysis with AntConc for social media data: intro, files and settings"},"content":{"rendered":"

Broadly defined, (computational) text analysis is a set of techniques for automated content analysis. Even without the use of complex statistics or computational analysis, social science researchers can improve their data exploration with techniques involving word counting, co-occurrence and collocations. \"\"<\/a><\/strong><\/p>\n

AntConc<\/a>\u00a0<\/strong>is one of the most easy-to-use and useful tools for text analysis and corpus linguistics. It was developed by Laurence Anthony<\/a>,\u00a0Professor in the Faculty of Science and Engineering at Waseda University, Japan. He maintains dozens of tools in his website like TagAnt<\/a> and FireAnt<\/a>.<\/p>\n

After this\u00a0intro<\/strong> on AntConc, we are going to see the following posts covering its main functionalities:<\/p>\n

    \n
  1. Intro, Opening a File and Settings<\/strong> (we are here)<\/li>\n
  2. Word Lists and File Viewer<\/a><\/li>\n
  3. Concordancer and Concordance Plot<\/a><\/li>\n
  4. Clusters and N-Grams\u00a0(soon)<\/li>\n
  5. Collocations\u00a0(soon)<\/li>\n<\/ol>\n

     <\/p>\n

    The following matrix was proposed in the paper\u00a0Computational text analysis for social science: Model assumptions and<\/span><\/em><\/a> complexity<\/em> and summarizes the possibilities between simple statistics\/computation x complex statistics\/computation and between weaker and stronger domain assumption. Using Antconc for analyze social media textual data encompasses simple statistics\/computation tasks such as word counting and statistics, but can be further applied on dictionary-based word counting by topic experts.<\/span><\/p>\n

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

    To understand and compare approaches from computer-aided content analysis, computer-aided interpretive textual analysis and corpus linguistics, I recommend the paper\u00a0Taming textual data: The contribution of corpus linguistics to computer-aided text analysis<\/em><\/a>.<\/p>\n

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

    AntConc<\/strong> will allow you to perform the main techniques of corpus linguistics such as Word Frequencies, Collocation, Concordance, N-Grams, Corpora Comparison to any kind of text.<\/p>\n

    \"\"<\/p>\n

    But first things first! Download AntConc<\/a> and read the following text, which will teach you the basics about the settings and how to open a file.<\/p>\n

     <\/p>\n

    How to collect social media textual data?<\/strong><\/p>\n

    There are dozens of social media research tools which allow to extract or monitor textual data on the main platforms.\u00a0<\/span>Most of them collect data through keyword\/hashtag search and\/or from specific pages and websites.\u00a0<\/span>The majority of the following tools uses UTF-8 encoding to export files in .csv format. You can open them with Excel or Libreoffice and copy-paste the desired texts to a notepad and save it as a .txt file.<\/span><\/p>\n

    Repositories\/curated lists of tools<\/span><\/i>:<\/span><\/p>\n