Topic modeling algorithms and applications: A survey

Abdelrazek, Aly; Eid, Yomna; Gawish, Eman; Mohamed, Hoda K.; Hassan, Ahmed

doi:10.1016/j.is.2022.102131

Cited by 94 publications

(43 citation statements)

References 34 publications

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“…Note that we use author keywords (i.e. the keywords chosen by the authors themselves) as a proxy for topics rather than extracting them automatically from full paper texts using topic modeling techniques (we refer the reader to [44] for a recent overview on topic modeling algorithms). The reason is that we purposely want to capture the topics explicitly selected by the authors to identify their work with.…”

Section: Topic Diversitymentioning

confidence: 99%

Measuring and Fostering Diversity in Affective Computing Research

Hupont

Tolan

Frau

et al. 2024

IEEE Trans. Affective Comput.

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This work presents a longitudinal study of diversity among the Affective Computing research community members. We explore several dimensions of diversity, including gender, geography, institutional types of affiliations and selected combinations of dimensions. We cover the last 10 years of the IEEE Transactions on Affective Computing (TAFFC) journal and the International Conference on Affective Computing and Intelligent Interaction (ACII), the primary sources of publications in Affective Computing. We also present an analysis of diversity among the members of the Association for the Advancement of Affective Computing (AAAC). Our findings reveal a "leaky pipeline" in the field, with a low -albeit slowly increasing over the years-representation of women. They also show that academic institutions clearly dominate publications, ahead of industry and governmental centres. In terms of geography, most publications come from the USA, contributions from Latin America or Africa being almost non-existent. Lastly, we find that diversity in the characteristics of researchers (gender and geographic location) influences diversity in the topics. To conclude, we analyse initiatives that have been undertaken in other AI-related research communities to foster diversity, and recommend a set of initiatives that could be applied to the Affective Computing field to increase diversity in its different facets. The diversity data collected in this work are publicly available, ensuring strict personal data protection and governance rules.

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Section: Topic Diversitymentioning

confidence: 99%

Measuring and Fostering Diversity in Affective Computing Research

Hupont

Tolan

Frau

et al. 2024

IEEE Trans. Affective Comput.

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“…The objective is to explore the hidden topic and the topic-word distributions, presumably describe the sample documents. An expression of the LDA model is described below: While some variations of LDA, Mallet LDA is considered for large corpus processing and analysis [7,9,11]. It focuses on scalability, If large corpus needs to analyze, Mallet LDA might be more suitable.…”

Section: Unsupervised Modeling With Ldamentioning

confidence: 99%

Exploratory analysis of suicidal tendency in depression investigation social media post

MOBIN,

MRIDHA,

MAHMUD

2024

Preprint

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Depression and suicide are interconnected. A depressive condition influences the chance of suicide. Though, to what extent of depression level triggers suicidal attempt is a scrutiny. The traditional interview-based clinical diagnostic approach is not effective for predicting a depressed person’s psychiatric status. Since patients often prefer not to disclose their emotions, they are reluctant to seek help from psychotherapists or doctors. Social media’s footprint often sees people discussing things that are not disclosed publicly. Emotionally distressed people often disclose themselves, seek empathy, and reveal physiological states. Hence, it has become a valuable source of research for depression and suicidal behaviors. This study uses social media datasets for exploratory data analysis to estimate the degree of suicidal thoughts within a depressed person’s post. The objective is to determine if a depressed individual has a suicidal tendency, determine the degree of intensity, or do the opposite. This study presents an unsupervised feature analysis using the topic model approach followed by supervised classification to quantify significance. Latent suicidal intents,cross-topic co-occurrence patterns, and dominant high-impact keywords of suicide are revealed from unsupervised Latent Dirichlet Allocation (LDA) modeling. Furthermore, supervised machine learning classifier models are applied to determine the severity of suicide tendencies. To get the best results, cutting-edge text embedding vectorization techniques and machine learning estimators are applied. Statistical measurements depict the degree of suicide intensity within the depressed label post. From the analysis, it is revealed that suicidal tendency among people with depression is extremely high. The depressed person’s post showed 60% similarities in various categories of suicidal intensity.

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“…In order to solve this problem, the intelligentization of spatial layout design has become a research hotspot in the academic circles. With the increasing maturity of computer-related technologies such as virtual reality technology and artificial intelligence, in order to enable users to have a more intuitive visual experience of interior design effects, and to meet users' needs for personalized interior space design, through virtual indoor three-dimensional roaming the system to simulate indoor scenes is supported by more and more people [3]. After inputting the floor plan of the apartment, the user can decorate and design the 3D reconstructed home scene through the interior space design software platform.…”

Section: Introductionmentioning

confidence: 99%

Research on intelligent design algorithm of indoor space based on hybrid recommendation model

2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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Looking at the traditional interior space design industry, the traditional design method is mainly manual design and the use of interactive modeling software and its design process mainly relies on trial and error. This paper takes the interior space design software platform as the background to study the collocation recommendation algorithm of the 3D home model, aim at improve the efficiency of the intelligent design algorithm. The recommendation idea of collaborative filtering is simple to implement, does not need to consider the inherent attribute characteristics of three-dimensional home projects, and is fast to calculate. After constructing the image feature database, this article uses the similarity between images to measure the visual similarity of the indoor space model; uses similar home projects to predict the collocation data of adjacent projects, and densifies the sparse collocation data; constructs each image separately Feature database, and use this to build its similarity table. According to the similarity table corresponding to each item, the first simNum items of the same category that are similar to the current item can be found. The experimental results show that compared with the traditional algorithm, the algorithm in this paper has greatly improved the accuracy of collocation recommendation.

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Topic modeling algorithms and applications: A survey

Cited by 94 publications

References 34 publications

Measuring and Fostering Diversity in Affective Computing Research

Measuring and Fostering Diversity in Affective Computing Research

Exploratory analysis of suicidal tendency in depression investigation social media post

Research on intelligent design algorithm of indoor space based on hybrid recommendation model

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