Computational Methods for Text Analysis
- provide basic understanding on how to properly use collections of texts as quantitative evidence, and to make this knowledge practical
- Being able to adequately interpret and report the results of computational text analysis in research papers.
- Being able to apply computational methods of text analysis (e.g. analysis of word frequency and co-occurrence, document classification, topic modeling) to collections of texts
- Being able to apply word embedding and clustering methods to downstream tasks, such as sentiment analysis, ideological scaling etc.
- Understanding multidimenional representation of lexical meaning and the role of the dimensionality reduction.
- Understanding possibilities of the automated text analysis as well as its pitfalls and important caveats about applying statistical tests to language data.
- Text Prepocessing
- Contrastive Analysis
- Text Classification
- Topic Modelling
- Word Embedding
- HomeworkStudens complete homeworks to emnsure a more complete understaning of materials discussed in the classroom.
- Practice work
- In-class assignment
- 2023/2024 2nd module0.15 * Homework + 0.3 * Practice work + 0.2 * In-class assignment
- Bamman, D., Eisenstein, J., & Schnoebelen, T. (2012). Gender identity and lexical variation in social media. https://doi.org/10.1111/josl.12080
- Jurafsky, D., Chahuneau, V., Routledge, B. R., & Smith, N. A. (2014). Narrative framing of consumer sentiment in online restaurant reviews. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.18543C32
- Text analysis in R. (2017). Communication Methods and Measures, 11(4), 245–265. https://doi.org/10.1080/19312458.2017.1387238