Computational Methods for Text Analysis
Maslinsky, Kirill A.
- provide basic understanding on how to properly use collections of texts as quantitative evidence, and to make this knowledge practical
- Understanding possibilities of the automated text analysis as well as its pitfalls and important caveats about applying statistical tests to language data.
- Understanding multidimenional representation of lexical meaning and the role of the dimensionality reduction.
- 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.
- Being able to adequately interpret and report the results of computational text analysis in research papers.
- Style — Document classification
- Content — Topic modeling
- Sentiment — Sentiment analysis
- Structure — Entities extraction
- Сourse participation0.3* paper summaries/presentations + 0.2 * in-class participation + 0.2 * homework + 0.3 mid-term test
- Final project
- Bamman, D., Eisenstein, J., & Schnoebelen, T. (2014). Gender identity and lexical variation in social media[The resear]. Journal of Sociolinguistics, 18(2), 135–160. 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