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        <title>Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers</title>
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            <title>Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)</title>
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            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers
%A Nguyen, Hanh
%A Hovy, Dirk
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F nguyen-hovy-2019-hey
%X 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.
%R 10.18653/v1/D19-5510
%U https://aclanthology.org/D19-5510
%U https://doi.org/10.18653/v1/D19-5510
%P 76-83

Markdown (Informal)

[Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers](https://aclanthology.org/D19-5510) (Nguyen & Hovy, WNUT 2019)

• Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers (Nguyen & Hovy, WNUT 2019)

ACL

• Hanh Nguyen and Dirk Hovy. 2019. Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 76–83, Hong Kong, China. Association for Computational Linguistics.