Challenge 20 - Topics in Hotel Reviews
Description: You work for a travel agency and want to better understand how hotels are reviewed online. What topics are common in the reviews as a whole, and what terms are most relevant in each topic? How about when you separate the reviews per rating? A colleague has already crawled and preprocessed the reviews for you, so your job now is to identify relevant topics in the reviews, and explore their key terms. What do the reviews uncover? Hint: Topic Extraction can be very helpful in tackling this challenge. Hint 2: Coherence and perplexity are metrics that can help you pick a meaningful number of topics.
URL: Optimizing semantic coherence in topic models - Mimno et al 2011, Proceedings of the Conference on Empirical Methods in Natural Language Processing 2011 https://dl.acm.org/doi/10.5555/2145432.2145462
URL: Summarizing topical content with word frequency and exclusivity - Bischof and Airoldi (2012), Proceedings of the 29th International Coference on International Conference on Machine Learning https://dl.acm.org/doi/10.5555/3042573.3042578
URL: Topic Scorer (Labs) - KNIME Community Hub https://hub.knime.com/-/spaces/-/latest/~5_W2h2g6hBY_M0Bc/
URL: Verified Components project - knime.com https://www.knime.com/verified-components
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