Search results for "Topic Model"

Isotopic Anthropology of Rural German Medieval Diet: Intra- and Inter-population Variability

2016

This study investigates the diet of an eleventh century CE parish community located in northwestern Germany. We assessed the isotopic compositions of human (n = 24) and faunal (n = 17) bone collagen (δ 13Ccol, δ 15Ncol) and human structural carbonate (δ 13Csc) using skeletal material recovered from the Dalheim cemetery. Traditional interpretation of the isotopic data indicates that Dalheim residents likely relied on a C3 plant-based diet and consumed some terrestrial animal products without evidence of marine resource input in the diet. Bivariate and multivariate models used as an additional means to assess diet indicate minor consumption of C4 plant foods in this community. The multivariat…

Automated Content Analysis of Destination Image: a Case Study

2020

Automated content analysis has become one of the most used approaches to extract “hidden” dimensions from text corpora over the last years. One of the data analysis techniques belonging to this approach is topic modeling, which can be fruitfully used to analyse complex phenomena like tourist destination image. With this aim in mind, this paper discusses the use of topic modeling to identify the main components of the image of cruise holidays spread through a specific type of visual text, i.e. the Television commercial. In order to achieve this goal, the paper presents the methodology and main results of a study carried out over a sample of TV commercials, which have recently been broadcast …

2021

Staying at the front line in learning research is challenging because many fields are rapidly developing. One such field is research on the temporal aspects of computer-supported collaborative learning (CSCL). To obtain an overview of these fields, systematic literature reviews can capture patterns of existing research. However, conducting systematic literature reviews is time-consuming and do not reveal future developments in the field. This study proposes a machine learning method based on topic modelling that takes articles from a systematic literature review on the temporal aspects of CSCL (49 original articles published before 2019) as a starting point to describe the most recent deve…

Establishing Video Game Genres Using Data-Driven Modeling and Product Databases

2015

Establishing genres is the first step toward analyzing games and how the genre landscape evolves over the years. We use data-driven modeling that distils genres from textual descriptions of a large collection of games. We analyze the evolution of game genres from 1979 till 2010. Our results indicate that until 1990, there have been many genres competing for dominance, but thereafter sport-racing, strategy, and action have become the most prevalent genres. Moreover, we find that games vary to a great extent as to whether they belong mostly to one genre or to a combination of several genres. We also compare the results of our data-driven model with two product databases, Metacritic and Mobyga…

H∞ fuzzy control of DC-DC converters with input constraint

2012

Published version of an article in the journal: Mathematical Problems in Engineering. Also available from the publisher at: http://dx.doi.org/10.1155/2012/973082 Open access This paper proposes a method for designing H∞ fuzzy control of DC-DC converters under actuator saturation. Because linear control design methods do not take into account the nonlinearity of the system, a T-S fuzzy model and a controller design approach is used. The designed control not only handles the external disturbance but also the saturation of duty cycle. The input constraint is first transformed into a symmetric saturation which is represented by a polytopic model. Stabilization conditions for the H∞ state feedba…

Supervised vs Unsupervised Latent DirichletAllocation: topic detection in lyrics.

2020

Topic modeling is a type of statistical modeling for discovering the abstract ``topics'' that occur in a collection of documents. Latent Dirichlet Allocation (LDA) is an example of topic model and is used to classify text in a document to a particular topic. It builds a fixed number of topics starting from words in each document modeled according to a Dirichlet distribution. In this work we are going to apply LDA to a set of songs from four famous Italian songwriters and split them into topics. This work studies the use of themes in lyrics using statistical analysis to detect topics. Aim of the work is to underline the main limits of the standard unsupervised LDA and to propose a supervised…

Exploring the challenges of remote work on Twitter users’ sentiments: From digital technology development to a post-pandemic era

2022

The boost in the use and development of technology, spurred by COVID-19 pandemic and its consequences, has sped up the adoption of new technologies and digital platforms in companies. Specifically, companies have been forced to change their organizational and work structures. In this context, the present study aims to identify the main opportunities and challenges for remote work through the use of digital technologies and platforms based on the analysis of user-generated content (UGC) in Twitter. Using computer-aided text analysis (CATA) and natural language processing (NLP), in this study, we conduct a sentiment analysis developed with Textblob, which works with machine learning. We then …

Statistically Validated Networks for assessing topic quality in LDA models

2022

Probabilistic topic models have become one of the most widespread machine learning technique for textual analysis purpose. In this framework, Latent Dirichlet Allocation (LDA) (Blei et al., 2003) gained more and more popularity as a text modelling technique. The idea is that documents are represented as random mixtures over latent topics, where a distribution overwords characterizes each topic. Unfortunately, topic models do not guarantee the interpretability of their outputs. The topics learned from the model may be only characterized by a set of irrelevant or unchained words, being useless for the interpretation. Although many topic-quality metrics were proposed (Newman et al., 2009; Alet…

Statistically Validated Networks for evaluating coherence in topic models

2022

Probabilistic topic models have become one of the most widespread machine learning technique for textual analysis purpose. In this framework, Latent Dirichlet Allocation (LDA) gained more and more popularity as a text modelling technique. The idea is that documents are represented as random mixtures over latent topics, where a distribution over words characterizes each topic. Unfortunately, topic models do not guarantee the interpretability of their outputs. The topics learned from the model may be characterized by a set of irrelevant or unchained words, being useless for the interpretation. In the framework of topic quality evaluation, the pairwise semantic cohesion among the top-N most pr…

MEASURING TOPIC COHERENCE THROUGH STATISTICALLY VALIDATED NETWORKS

2020

Topic models arise from the need of understanding and exploring large text document collections and predicting their underlying structure. Latent Dirichlet Allocation (LDA) (Blei et al., 2003) has quickly become one of the most popular text modelling techniques. The idea is that documents are represented as random mixtures over latent topics, where a distribution over words characterizes each topic. Unfortunately, topic models give no guaranty on the interpretability of their outputs. The topics learned from texts may be characterized by a set of irrelevant or unchained words. Therefore, topic models require validation of the coherence of estimated topics. However, the automatic evaluation …