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
- 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