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I'm looking to try and use deep learning methods for topic modeling as opposed to the more traditional methods of lda and word embedding methods. However, I'm having trouble finding good labeled datasets for this task. So far the best that I've seen is the New York Times Dataset which I can't use due to licensing constraints. I've also seen the 20News Dataset but it only has twenty categories so it probably won't scale well to other domains.

Are there any other good datasets out there that I'm missing that can be used for topic modeling? I'm happy to use a dataset that isn't explicitly meant for topic modeling; as long as it has some sentences/paragraphs that are tagged or labeled that should be fine.

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Julian McAuley of UCSD compiles a list of datasets for recommender systems. These are a diverse set of labelled text data fortified with additional measures available for various datasets (e.g. network relationships, geospatial data, categorical information, quantitative measures, etc.).

One dataset that has been frequently used in topic modelling and other natural language processing tasks is the Amazon Reviews Dataset (versions 2013, 2014 and 2018). Excerpts from the 2019 dataset overview:

... this dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs)...

... The total number of reviews is 233.1 million (142.8 million in 2014)...

... Current data includes reviews in the range May 1996 - Oct 2018...

... We have added transaction metadata for each review shown on the review page...

The original study that generated and used this dataset is highly cited on Google Scholar.

McAuley, J. and Leskovec, J., 2013, October. Hidden factors and hidden topics: understanding rating dimensions with review text. In Proceedings of the 7th ACM conference on Recommender systems (pp. 165-172). ACM.

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