Topic Modeling and Sentiment Analysis of Social Media Data to Drive Experiential Redesign

Abstract

The elicitation of customer pain points is a crucial early step in the design or redesign of successful products and services. Online, user-generated data contains rich, real-time information about customer experience, requirements, and preferences. However, it is a nontrivial task to retrieve useful information from these sources because of the sheer amount of data, often unstructured. In this work, we build on previous efforts that used natural language processing techniques to extract meaning from online data and facilitate experiential redesign and extend them by integrating a sentiment analysis. As a use case, we explore the airline industry. A considerable portion of potential passengers opt out of traveling by airplane due to aviophobia, a fear of flying. This causes a market loss to the industry and inconvenience for those who experience aviophobia. The potential contributors to aviophobia are complex and diverse, involving physical, psychological and emotional reactions to the air travel experience. A methodology that is capable of accommodating the complexity and diversity of the commercial airline industry user-generated data is necessary to effectively mine customer pain points. To address the demand, we propose a novel methodology in this study. Using passenger commentary data posted on Reddit, the method implements topic modeling to extract common themes from the commentaries and employs sentiment analysis to elicit and interpret the salient information contained in the extracted themes. This paper ends by providing specific recommendations that are germane to the use case as well as suggesting future research directions.

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

“…While this length is still relatively short, GSDMM has been shown to be accurate on tweets, which were a maximum of 140 characters (~20 words), or more recently, 280 (Yin & Wang 2014). While the number of documents is smaller than the dataset used in the GSDMM debut paper (Yin & Wang 2014), we believe that it is an appropriate value for an exploratory study, and other researchers have demonstrated success using traditional topic models with a similar number of documents to our dataset (Fu et al 2010; Song et al 2020; Gyory et al 2021 a ). Nonetheless, we tested our results for robustness with larger and smaller document definitions and found our results to be consistent.

Figure 4. Plots displaying the characteristics of the dataset in terms of channel-day documents, postprocessing.

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“…Specifically, topic modelling has been used in product design for studying the impact of various interventions on design (Fu, Cagan & Kotovsky 2010; Gyory, Kotovsky & Cagan 2021 a ), to comparing human and AI teams (Gyory et al 2021 b ), to analysing capstone team performance (Ball, Bessette & Lewis 2020), to visualising engineering student identity (Park et al 2020), to deriving new product design features from online reviews (Song et al 2020; Zhou et al 2020) and identifying areas for cross-domain inspiration (Ahmed & Fuge 2018). By plotting a design team’s topic mixtures before and after a manager intervention, Gyory et al (2021 a ) were able to measure the result of the design intervention and whether it helped to bring the team back on track.…”

Design Knowledge Representation With Technology Semantic Network

“…Traditional topic mapping and modelling methods reveal the common topics in a set of documents and latent semantic structures in the documents by employing mainly Latent Semantic Analysis (LSA) (Deerwester et al, 1990) or Latent Dirichlet Allocation (LDA) (Blei et al, 2003). These topic modelling methods have been extensively employed in the engineering design literature to create structured design repositories (Fu et al, 2013), aid prior art or document search (Krestel and Smyth, 2013), enable the analysis of longitudinal changes in a field (Chiarello et al, 2019), and support the innovative product design processes (Dong et al, 2004;Song, Meinzer, et al, 2020). Studies using traditional topic modelling methods provide more coarse information about documents and their contents, which can be used to map them to meaningful groups in a set of documents.…”

“…Automatic summary representation of design-related topics or entities in technical design documents is an important task in engineering design since it can inform designers in various tasks in different phases of the design process (Dong and Agogino, 1996;Szykman et al, 2000). For instance, engineering design researchers have studied the topics in large design repositories to reveal the prominent and emerging fields (Chiarello et al, 2019;Song, Yan, et al, 2019), or to discover the structure of these repositories and enable the search for prior arts and design inspiration (or stimuli) in the early design stages (Fu et al, 2013;Song, Meinzer, et al, 2020). Topic mapping methods can provide various insights, such as most frequently addressed topics or particular topics within a collection of documents (Řehůřek and Sojka, 2010).…”