Chapter 21 From Frequency Counts to Contextualized Word Embeddings
Text, the written representation of human thought and communication in natural language, has been a major source of data for social science research since its early beginnings. While quantitative approaches seek to make certain contents measurable, for example through word counts or reliable categor...
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| Format: | Online |
| Idioma: | anglès |
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Taylor & Francis
2022
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| author | Wiedemann, Gregor Fedtke, Cornelia |
| author_browse | Fedtke, Cornelia Wiedemann, Gregor |
| author_facet | Wiedemann, Gregor Fedtke, Cornelia |
| author_sort | Wiedemann, Gregor |
| collection | Directory of Open Access Books |
| description | Text, the written representation of human thought and communication in natural language, has been a major source of data for social science research since its early beginnings. While quantitative approaches seek to make certain contents measurable, for example through word counts or reliable categorization (coding) of longer text sequences, qualitative social researchers put more emphasis on systematic ways to generate a deep understanding of social phenomena from text. For the latter, several qualitative research methods such as qualitative content analysis (Mayring, 2010), grounded theory methodology (Glaser & Strauss, 2005), and (critical) discourse analysis (Foucault, 1982) have been developed. Although their methodological foundations differ widely, both currents of empirical research need to rely to some extent on the interpretation of text data against the background of its context. At the latest with the global expansion of the internet in the digital era and the emergence of social networks, the huge mass of text data poses a significant problem to empirical research relying on human interpretation. For their studies, social scientists have access to newspaper texts representing public media discourse, web documents from companies, parties, or NGO websites, political documents from legislative processes such as parliamentary protocols, bills and corresponding press releases, and for some years now micro-posts and user comments from social media. Computational support is inevitable even to process samples of such document volumes that could easily comprise millions of documents. |
| format | Online |
| id | doab-20.500.12854ir-94629 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Taylor & Francis |
| publisherStr | Taylor & Francis |
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| spelling | doab-20.500.12854ir-946292025-08-13T13:41:54Z Chapter 21 From Frequency Counts to Contextualized Word Embeddings Wiedemann, Gregor Fedtke, Cornelia survey data, data analysis, data science, information technology, AI, socio-robotics, quantitative, survey methodology, ethics, ethical standards, privacy, replication, politics, survey design, social media, big data, social, human-robot interaction, machine learning, open data, data archives, data ownership, digital trace, unstructured data thema EDItEUR::J Society and Social Sciences::JM Psychology thema EDItEUR::J Society and Social Sciences::JM Psychology::JMB Psychological methodology thema EDItEUR::J Society and Social Sciences::JM Psychology thema EDItEUR::J Society and Social Sciences::JM Psychology::JMB Psychological methodology Text, the written representation of human thought and communication in natural language, has been a major source of data for social science research since its early beginnings. While quantitative approaches seek to make certain contents measurable, for example through word counts or reliable categorization (coding) of longer text sequences, qualitative social researchers put more emphasis on systematic ways to generate a deep understanding of social phenomena from text. For the latter, several qualitative research methods such as qualitative content analysis (Mayring, 2010), grounded theory methodology (Glaser & Strauss, 2005), and (critical) discourse analysis (Foucault, 1982) have been developed. Although their methodological foundations differ widely, both currents of empirical research need to rely to some extent on the interpretation of text data against the background of its context. At the latest with the global expansion of the internet in the digital era and the emergence of social networks, the huge mass of text data poses a significant problem to empirical research relying on human interpretation. For their studies, social scientists have access to newspaper texts representing public media discourse, web documents from companies, parties, or NGO websites, political documents from legislative processes such as parliamentary protocols, bills and corresponding press releases, and for some years now micro-posts and user comments from social media. Computational support is inevitable even to process samples of such document volumes that could easily comprise millions of documents. 2022-12-07T04:03:59Z 2022-12-07T04:03:59Z 2022-12-06T10:27:37Z 2022 chapter https://library.oapen.org/handle/20.500.12657/59856 9780367457808 9781032077703 https://directory.doabooks.org/handle/20.500.12854/94629 eng open access image/jpeg image/jpeg image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International Attribution-NonCommercial-NoDerivatives 4.0 International Attribution-NonCommercial-NoDerivatives 4.0 International https://library.oapen.org/bitstream/20.500.12657/59856/1/9781003025245_10.4324_9781003025245-25.pdf https://library.oapen.org/bitstream/20.500.12657/59856/1/9781003025245_10.4324_9781003025245-25.pdf https://library.oapen.org/bitstream/20.500.12657/59856/1/9781003025245_10.4324_9781003025245-25.pdf Taylor & Francis Routledge 10.4324/9781003025245-25 10.4324/9781003025245-25 fa69b019-f4ee-4979-8d42-c6b6c476b5f0 Handbook of Computational Social Science, Volume 2 9780367457808 9781032077703 Routledge 21 open access |
| spellingShingle | survey data, data analysis, data science, information technology, AI, socio-robotics, quantitative, survey methodology, ethics, ethical standards, privacy, replication, politics, survey design, social media, big data, social, human-robot interaction, machine learning, open data, data archives, data ownership, digital trace, unstructured data thema EDItEUR::J Society and Social Sciences::JM Psychology thema EDItEUR::J Society and Social Sciences::JM Psychology::JMB Psychological methodology thema EDItEUR::J Society and Social Sciences::JM Psychology thema EDItEUR::J Society and Social Sciences::JM Psychology::JMB Psychological methodology Wiedemann, Gregor Fedtke, Cornelia Chapter 21 From Frequency Counts to Contextualized Word Embeddings |
| title | Chapter 21 From Frequency Counts to Contextualized Word Embeddings |
| title_full | Chapter 21 From Frequency Counts to Contextualized Word Embeddings |
| title_fullStr | Chapter 21 From Frequency Counts to Contextualized Word Embeddings |
| title_full_unstemmed | Chapter 21 From Frequency Counts to Contextualized Word Embeddings |
| title_short | Chapter 21 From Frequency Counts to Contextualized Word Embeddings |
| title_sort | chapter 21 from frequency counts to contextualized word embeddings |
| topic | survey data, data analysis, data science, information technology, AI, socio-robotics, quantitative, survey methodology, ethics, ethical standards, privacy, replication, politics, survey design, social media, big data, social, human-robot interaction, machine learning, open data, data archives, data ownership, digital trace, unstructured data thema EDItEUR::J Society and Social Sciences::JM Psychology thema EDItEUR::J Society and Social Sciences::JM Psychology::JMB Psychological methodology thema EDItEUR::J Society and Social Sciences::JM Psychology thema EDItEUR::J Society and Social Sciences::JM Psychology::JMB Psychological methodology |
| topic_facet | survey data, data analysis, data science, information technology, AI, socio-robotics, quantitative, survey methodology, ethics, ethical standards, privacy, replication, politics, survey design, social media, big data, social, human-robot interaction, machine learning, open data, data archives, data ownership, digital trace, unstructured data thema EDItEUR::J Society and Social Sciences::JM Psychology thema EDItEUR::J Society and Social Sciences::JM Psychology::JMB Psychological methodology thema EDItEUR::J Society and Social Sciences::JM Psychology thema EDItEUR::J Society and Social Sciences::JM Psychology::JMB Psychological methodology |
| url | https://library.oapen.org/handle/20.500.12657/59856 |
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