Intrinsic and Extrinsic Evaluation of Word Embedding Models

Yesiltas, Gokce; Güngör, Tunga

doi:10.1109/asyu50717.2020.9259855

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Similarly, we did not use the terms user , and companies as keywords. While word embeddings capture syntactic regularities such as singular/plural forms (Mikolov et al, 2013;Yeşiltaş and Güngör, 2020), we reason that this methodological decision should not have affected considerably our results. Nevertheless, future work could include plural and singular versions of the same term to confirm this hypothesis.…”

Section: Discussionmentioning

confidence: 87%

Regional Differences in Information Privacy Concerns After the Facebook-Cambridge Analytica Data Scandal

González-Pizarro

Figueroa

López

et al. 2022

Comput Supported Coop Work

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While there is increasing global attention to data privacy, most of their current theoretical understanding is based on research conducted in a few countries. Prior work argues that people's cultural backgrounds might shape their privacy concerns; thus, we could expect people from different world regions to conceptualize them in diverse ways. We collected and analyzed a large-scale dataset of tweets about the #CambridgeAnalytica scandal in Spanish and English to start exploring this hypothesis. We employed word embeddings and qualitative analysis to identify which information privacy concerns are present and characterize language and regional differences in emphasis on these concerns. Our results suggest that related concepts, such as regulations, can be added to current information privacy frameworks. We also observe a greater emphasis on data collection in English than in Spanish. Additionally, data from North America exhibits a narrower focus on awareness compared to other regions under study. Our results call for more diverse sources of data and nuanced analysis of data privacy concerns around the globe.

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Section: Discussionmentioning

confidence: 87%

Regional Differences in Information Privacy Concerns After the Facebook-Cambridge Analytica Data Scandal

González-Pizarro

Figueroa

López

et al. 2022

Comput Supported Coop Work

View full text Add to dashboard Cite

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An Analysis of Intelligent Turkish Text Classification Models for Routing Calls in Call Centers: A Case Study on the Republic of Turkiye Ministry of Trade Call Center

Özdemir,

Ortakcı

2024

Sakarya University Journal of Computer and Information Sciences

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Call centers play a key role in the management of customer relationships in the modern business world. However, the growing demand for their services presents significant challenges, particularly in terms of staffing and handling increasing call volumes. This paper addresses these issues by presenting an AI-driven text classification framework tailored for the Republic of Turkiye Ministry of Trade Call Centre (MTCC), with the aim of automatically routing calls to relevant departments. Using a specific dataset of 20,000 phone call texts collected from the MTCC, the study employs TF-IDF, Word2Vec, and GloVe text vectorization techniques and applies various machine learning algorithms such as K-Nearest Neighbours, Naive Bayes, Support Vector Machines, Adaptive Boosting, Decision Tree and Random Forest for text classification. Through a comprehensive analysis, the study answers key research questions regarding optimal classifiers and vectorization methods. The proposed solution not only improves the efficiency of MTCC's call routing but also provides researchers with practical insights regarding Turkish text classification. The results indicate that a combination of the Random Forest classifier and Word2Vec text vectorization method is the optimal model that can manage to route calls in real-time.

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Intrinsic and Extrinsic Evaluation of Word Embedding Models

Cited by 2 publications

References 15 publications

Regional Differences in Information Privacy Concerns After the Facebook-Cambridge Analytica Data Scandal

Regional Differences in Information Privacy Concerns After the Facebook-Cambridge Analytica Data Scandal

An Analysis of Intelligent Turkish Text Classification Models for Routing Calls in Call Centers: A Case Study on the Republic of Turkiye Ministry of Trade Call Center

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