TOMODAPI: A Topic Modeling API to Train, Use and Compare Topic Models

Lisena, Pasquale; Harrando, Ismail; Kandakji, Oussama; Troncy, Raphaël

doi:10.18653/v1/2020.nlposs-1.19

Cited by 12 publications

(13 citation statements)

References 22 publications

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“…We measured the intrinsic coherence of each model using normalised pointwise mutual information (NPMI) (Eq. ( 2)) (Lisena et al 2020), which traditionally has a strong correlation with human ratings (Röder et al 2015). This was done using the TOMODAPI (Lisena et al 2020), which applies Eq.…”

Section: Evaluating Convergence Patternsmentioning

confidence: 99%

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Communication patterns in engineering enterprise social networks: an exploratory analysis using short text topic modelling

et al. 2022

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Enterprise social network messaging sites are becoming increasingly popular for team communication in engineering and product design. These digital communication platforms capture detailed messages between members of the design team and are an appealing data set for researchers who seek to better understand communication in design. This exploratory study investigates whether we can use enterprise social network messages to model communication patterns throughout the product design process. We apply short text topic modelling (STTM) to a data set comprising 250,000 messages sent by 32 teams enrolled in a 3-month intensive product design course. Many researchers describe the engineering design process as a series of convergent and divergent thinking stages, such as the popular double diamond model, and we use this theory as a case study in this work. Quantitative and qualitative analysis of STTM results reveals several trends, such as it is indeed possible to see evidence of cyclical convergence and divergence of topics in team communication; within the convergence–divergence pattern, strong teams have fewer topics in their topic models than weaker teams; and teams show characteristics of product, project, course, and other themes within each topic. We provide evidence that the analysis of enterprise social networking messages, with advanced topic modelling techniques, can uncover insights into design processes and can identify the communication patterns of successful teams.

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Section: Evaluating Convergence Patternsmentioning

confidence: 99%

“…( 2)) (Lisena et al 2020), which traditionally has a strong correlation with human ratings (Röder et al 2015). This was done using the TOMODAPI (Lisena et al 2020), which applies Eq. ( 2) to couples of words, computing their joint probabilities.…”

Section: Evaluating Convergence Patternsmentioning

confidence: 99%

Communication patterns in engineering enterprise social networks: an exploratory analysis using short text topic modelling

et al. 2022

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“…ToModAPI (Lisena et al, 2020) is a python API that allows for training, inference, and evaluating different topic models, also including some of the most recent. However, it does not provide a method for finding the best hyper-parameter configuration of topic models.…”

Section: Existing Frameworkmentioning

confidence: 99%

“…Current topic modeling frameworks (McCallum et al, 2005;Qiang et al, 2018;Lisena et al, 2020) typically focus on the release of topic modeling algorithms while ignoring one or more critical aspects of the topic modeling pipeline, such as preprocessing, evaluation, comparison of the models, and visualization. Most importantly, they disregard the hyper-parameter selection.…”

Section: Introductionmentioning

confidence: 99%

OCTIS: Comparing and Optimizing Topic models is Simple!

Terragni¹,

Fersini²,

Galuzzi³

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrati

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In this paper, we present OCTIS, a framework for training, analyzing, and comparing Topic Models, whose optimal hyper-parameters are estimated using a Bayesian Optimization approach. The proposed solution integrates several state-of-the-art topic models and evaluation metrics. These metrics can be targeted as objective by the underlying optimization procedure to determine the best hyper-parameter configuration. OCTIS allows researchers and practitioners to have a fair comparison between topic models of interest, using several benchmark datasets and well-known evaluation metrics, to integrate novel algorithms, and to have an interactive visualization of the results for understanding the behavior of each model. The code is available at the following link: https://github.com/ MIND-Lab/OCTIS.

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“…The full set of parameters is documented in the repository 7 ; • For each trained model, we compute all the intrinsic (coherence) metrics and the groundtruth-based ones. For the experiment, we rely on To-ModAPI (Lisena et al, 2020), an open-source topic modelling API that is built to easily train, evaluate and compare several topic models. This framework provides a common interface for training, performing topic inference, and evaluating using coherence and ground truth.…”

Section: Varying the Datasetsmentioning

confidence: 99%