Human Perception of Enriched Topic Models

Lukasiewicz, Wojciech; Todor, Alexandru; Paschke, Adrian

doi:10.1007/978-3-319-93931-5_2

Cited by 2 publications

(1 citation statement)

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“…This metric is preferred for the topic intrusion task rather than the model precision, which only takes into account right or wrong answers, because each topic has a probability of generating the review. Thus, the topic log odds serve as an error function (Lukasiewicz et al, 2018). 4 Results and discussion…”

Section: Lda Validationmentioning

confidence: 99%

Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers

Nguyen¹,

Hovy²

2019

Proceedings of the 5th Workshop on Noisy User-Generated Text (W-Nut 2019)

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User reviews provide a significant source of information for companies to understand their market and audience. In order to discover broad trends in this source, researchers have typically used topic models such as Latent Dirichlet Allocation (LDA). However, while there are metrics to choose the "best" number of topics, it is not clear whether the resulting topics can also provide in-depth, actionable product analysis. Our paper examines this issue by analyzing user reviews from the Best Buy US website for smart speakers. Using coherence scores to choose topics, we test whether the results help us to understand user interests and concerns. We find that while coherence scores are a good starting point to identify a number of topics, it still requires manual adaptation based on domain knowledge to provide market insights. We show that the resulting dimensions capture brand performance and differences, and differentiate the market into two distinct groups with different properties.

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Section: Lda Validationmentioning

confidence: 99%