@article{
 title = {An Evaluation of Topic Modelling Techniques for Twitter},
 type = {article},
 websites = {https://www.cs.toronto.edu/~jstolee/projects/topic.pdf},
 id = {e0d247b8-f815-34eb-a55a-832250e28a0d},
 created = {2018-02-05T17:31:26.539Z},
 accessed = {2018-02-05},
 file_attached = {true},
 profile_id = {371589bb-c770-37ff-8193-93c6f25ffeb1},
 group_id = {f982cd63-7ceb-3aa2-ac7e-a953963d6716},
 last_modified = {2018-02-05T17:31:28.692Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper, we complete an evaluation of various topic modelling algorithms, and examine their performance when working with Twitter tweets. LDA [1] is an algorithm that is often used when modelling topics within text, and it has been proven to be affective; however, LDA may not necessarily perform well when working with documents that are short in length [2, 3, 4]. We compare LDA to three models which offer potential improvements over the downfalls of LDA when modelling tweets. This includes a variation of LDA, referred to as LDA-U, which aggregates data on a user-basis in an effort to improve the standard LDA model's performance[3]. We also evaluate two other models specifically designed to work with short text: the " biterm topic model " (BTM), and a " word2vec Gaussian mix-ture model " , which models topics as a distribution over words in semantic space [4].},
 bibtype = {article},
 author = {Jónsson, Elías and Stolee, Jake}
}

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