TY - CONF TI - Twitter Topic Modeling by Tweet Aggregation AU - Steinskog, Asbjørn AU - Therkelsen, Jonas AU - Gambäck, Björn T2 - 21st Nordic Conference of Computational Linguistics AB - Conventional topic modeling schemes, such as Latent Dirichlet Allocation, are known to perform inadequately when applied to tweets, due to the sparsity of short documents. To alleviate these disadvantages, we apply several pooling techniques, aggregating similar tweets into individual documents, and specifically study the aggregation of tweets sharing authors or hashtags. The results show that aggregating similar tweets into individual documents significantly increases topic coherence. C1 - Gothenburg C3 - Proceedings of the 21st Nordic Conference of Computational Linguistics DA - 2017/// PY - 2017 DP - Semantic Scholar SP - 77 EP - 86 LA - en PB - Linko¨ping University Electronic Press UR - https://www.semanticscholar.org/paper/Twitter-Topic-Modeling-by-Tweet-Aggregation-Steinskog-Therkelsen/89735b06ee5d7bcb469ddc619022bbc9f2443f02 KW - Social media analysis KW - Text Analysis ER -