2021
Lu, Chien; Peltonen, Jaakko; Nummenmaa, Timo; Nummenmaa, Jyrki; Järvelin, Kalervo
Cross-structural Factor-topic Model: Document Analysis with Sophisticated Covariates
In: Balasubramanian, Vineeth N.; Tsang, Ivor (Ed.): Proceedings of the 13th Asian Conference on Machine Learning, pp. 1129-1144, Asian Conference on Machine Learning, 2021, ISSN: 2640-3498.
In proceedings Open access
Abstract | Links | Tags: Natural language processing, Probabilistic modeling, Topic modeling
@inproceedings{Lu2021b,
title = {Cross-structural Factor-topic Model: Document Analysis with Sophisticated Covariates},
author = {Chien Lu and Jaakko Peltonen and Timo Nummenmaa and Jyrki Nummenmaa and Kalervo Järvelin},
editor = {Vineeth N. Balasubramanian and Ivor Tsang},
url = {https://urn.fi/URN:NBN:fi:tuni-202201241538},
issn = {2640-3498},
year = {2021},
date = {2021-11-17},
urldate = {2021-11-17},
booktitle = {Proceedings of the 13th Asian Conference on Machine Learning},
pages = {1129-1144},
publisher = {Asian Conference on Machine Learning},
abstract = {Modern text data is increasingly gathered in situations where it is paired with a high-dimensional collection of covariates: then both the text, the covariates, and their relationships are of interest to analyze. Despite the growing amount of such data, current topic models are unable to take into account large amounts of covariates successfully: they fail to model structure among covariates and distort findings of both text and covariates. This paper presents a solution: a novel factor-topic model that enables researchers to analyze latent structure in both text and sophisticated document-level covariates collectively. The key innovation is that besides learning the underlying topical structure, the model also learns the underlying factorial structure from the covariates and the interactions between the two structures. A set of tailored variational inference algorithms for efficient computation are provided. Experiments on three different datasets show the model outperforms comparable topic models in the ability to predict held-out document content. Two case studies focusing on Finnish parliamentary election candidates and game players on Steam demonstrate the model discovers semantically meaningful topics, factors, and their interactions. The model both outperforms state-of-the-art models in predictive accuracy and offers new factor-topic insights beyond other topic models.},
keywords = {Natural language processing, Probabilistic modeling, Topic modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Modern text data is increasingly gathered in situations where it is paired with a high-dimensional collection of covariates: then both the text, the covariates, and their relationships are of interest to analyze. Despite the growing amount of such data, current topic models are unable to take into account large amounts of covariates successfully: they fail to model structure among covariates and distort findings of both text and covariates. This paper presents a solution: a novel factor-topic model that enables researchers to analyze latent structure in both text and sophisticated document-level covariates collectively. The key innovation is that besides learning the underlying topical structure, the model also learns the underlying factorial structure from the covariates and the interactions between the two structures. A set of tailored variational inference algorithms for efficient computation are provided. Experiments on three different datasets show the model outperforms comparable topic models in the ability to predict held-out document content. Two case studies focusing on Finnish parliamentary election candidates and game players on Steam demonstrate the model discovers semantically meaningful topics, factors, and their interactions. The model both outperforms state-of-the-art models in predictive accuracy and offers new factor-topic insights beyond other topic models.
2020
Lu, Chien; Koskinen, Elina; Leorke, Dale; Nummenmaa, Timo; Peltonen, Jaakko
In: Brooks, Anthony; Brooks, Eva Irene; Jonathan, Duckworth (Ed.): Interactivity and Game Creation: 9th EAI International Conference, ArtsIT 2020, Aalborg, Denmark, December 10–11, 2020, Proceedings, pp. 160-179, Springer, 2020, ISBN: 9783030734251.
In proceedings Open access
Links | Tags: Bibliometric analysis, Location-based game, Text mining, Topic modeling
@inproceedings{Lu2020d,
title = {The World Is Your Playground: A Bibliometric and Text Mining Analysis of Location-Based Game Research},
author = {Chien Lu and Elina Koskinen and Dale Leorke and Timo Nummenmaa and Jaakko Peltonen},
editor = {Anthony Brooks and Eva Irene Brooks and Duckworth Jonathan
},
url = {https://homepages.tuni.fi/jaakko.peltonen/online-papers/lu2020artsit.pdf},
doi = {10.1007/978-3-030-73426-8_9},
isbn = {9783030734251},
year = {2020},
date = {2020-12-10},
booktitle = {Interactivity and Game Creation: 9th EAI International Conference, ArtsIT 2020, Aalborg, Denmark, December 10–11, 2020, Proceedings},
pages = {160-179},
publisher = {Springer},
keywords = {Bibliometric analysis, Location-based game, Text mining, Topic modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Lu, Chien; Li, Xiaozhou; Nummenmaa, Timo; Zhang, Zheying; Peltonen, Jaakko
Patches and Player Community Perceptions: Analysis of No Man's Sky Steam Reviews
In: DiGRA ’20 – Proceedings of the 2020 DiGRA International Conference: Play Everywhere, DiGRA, 2020, ISSN: 2342-9666.
In proceedings Open access
Abstract | Links | Tags: No Man's Sky, Player modeling, Topic modeling
@inproceedings{Lu2020,
title = {Patches and Player Community Perceptions: Analysis of No Man's Sky Steam Reviews},
author = {Chien Lu and Xiaozhou Li and Timo Nummenmaa and Zheying Zhang and Jaakko Peltonen},
url = {http://urn.fi/URN:NBN:fi:tuni-202012048488},
issn = {2342-9666},
year = {2020},
date = {2020-06-02},
booktitle = {DiGRA ’20 – Proceedings of the 2020 DiGRA International Conference: Play Everywhere},
publisher = {DiGRA},
abstract = {Current game publishing typically involves an ongoing commitment to maintain and update games after initial release, and as a result the reception of games among players has the potential to evolve; it is then crucial to understand how players’ concerns and perception of the game are affected by ongoing updates and by passage of time in general. We carry out a data-driven analysis of a prominent game release, No Man’s Sky, using topic modeling based text mining of Steam reviews. Importantly, our approach treats player perception not as a single sentiment but identifies multiple topics of interest that evolve differently over time, and allows us to contrast patching of the game to evolution of the topics.},
keywords = {No Man's Sky, Player modeling, Topic modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Current game publishing typically involves an ongoing commitment to maintain and update games after initial release, and as a result the reception of games among players has the potential to evolve; it is then crucial to understand how players’ concerns and perception of the game are affected by ongoing updates and by passage of time in general. We carry out a data-driven analysis of a prominent game release, No Man’s Sky, using topic modeling based text mining of Steam reviews. Importantly, our approach treats player perception not as a single sentiment but identifies multiple topics of interest that evolve differently over time, and allows us to contrast patching of the game to evolution of the topics.
Lu, Chien; Peltonen, Jaakko; Nummenmaa, Timo; Li, Xiaozhou; Zhang, Zheying
What Makes a Trophy Hunter? An Empirical Analysis of Reddit Discussions
In: Koivisto, Jonna; Bujić, Mila; Hamari, Juho (Ed.): Proceedings of the 4th International GamiFIN Conference, pp. 146-156, CEUR-WS, 2020, ISSN: 1613-0073.
In proceedings Open access
Abstract | Links | Tags: Reddit, Topic modeling, Trophy hunting
@inproceedings{Lu2020c,
title = {What Makes a Trophy Hunter? An Empirical Analysis of Reddit Discussions},
author = {Chien Lu and Jaakko Peltonen and Timo Nummenmaa and Xiaozhou Li and Zheying Zhang},
editor = {Jonna Koivisto and Mila Bujić and Juho Hamari},
url = {https://urn.fi/URN:NBN:fi:tuni-202008266665},
issn = {1613-0073},
year = {2020},
date = {2020-04-01},
booktitle = {Proceedings of the 4th International GamiFIN Conference},
pages = {146-156},
publisher = {CEUR-WS},
abstract = {In this paper, an empirical data-driven analysis of online discussions of meta-game reward systems is carried out. The data is collected from one of the biggest online discussion forums called Reddit and a text-mining technique called topic modeling is employed. Over 46000 discussion threads from the two most relevant subreddits /r/xboxachievements and /r/Trophies are analyzed and the results of topic modeling shows not only interesting topics but also the (dis)similarity between two text sources the temporal trends of topics. We have found that the volume of related discussions shows an ongoing trend. The topic model results have also revealed that some game genres are more prevalent than others and the (dis)similarities between text sources have been also discovered.},
keywords = {Reddit, Topic modeling, Trophy hunting},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, an empirical data-driven analysis of online discussions of meta-game reward systems is carried out. The data is collected from one of the biggest online discussion forums called Reddit and a text-mining technique called topic modeling is employed. Over 46000 discussion threads from the two most relevant subreddits /r/xboxachievements and /r/Trophies are analyzed and the results of topic modeling shows not only interesting topics but also the (dis)similarity between two text sources the temporal trends of topics. We have found that the volume of related discussions shows an ongoing trend. The topic model results have also revealed that some game genres are more prevalent than others and the (dis)similarities between text sources have been also discovered.