Data-Driven Identification of German Phrasal Compounds

Barbaresi, Adrien; Hein, Katrin

doi:10.1007/978-3-319-64206-2_22

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Таким чином слід ретельно підбирати алгоритми стемінгу та лематизації твітів. Для використання в наступних етапах обрані інструменти tree_stem [16] для стемінгу та для лематизації -simplelemma [17], що ґрунтується на алгоритмах лексичного аналізу, описаних в [18], [19] та [20]. Відслідкуємо вплив на опрацювання твітів робить стемінг та лематизація на прикладі вибірки в 9000 твітів (табл.…”

Section: матеріали та методиunclassified

Ukrainian Language Tweets Analysis Technology for Public Opinion Dynamics Change Prediction Based on Machine Learning

Prokipchuk¹,

Vysotska²

2023

RIC

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Context. Automation of public opinion research will allow not only to reduce the amount of manual work, but also to obtain time slices of the results without additional efforts. Since direct interaction with respondents should be avoided, public opinion should be analyzed based on the sources of its free expression. Social networks are great for this role, as their people freely publish their thoughts or emotionally truthfully react to published information about certain events. Statistics show that data from social networks is not enough to obtain a full-fledged result, because a significant percentage of people do not use social networks. However, the automation of the study of even such a stratum of the population is already a good result for analyzing the dynamics of changes in public opinion in accordance with events in the country/world and, accordingly, for correcting the processes of public administration in the future. Objective of the study is to develop a technology for analyzing the flow of Ukrainian-language content in social networks for public opinion research based on finding clustered thematic groups of tweets. Method. The article develops a technology for finding tweet trends based on clustering, which forms a data stream in the form of short representations of clusters and their popularity for further research of public opinion. An effective approach to tweet collection, filtering, cleaning and pre-processing based on a comparative analysis of Bag of Words, TF-IDF and BERT algorithms is described. The impact of stemming and lemmatization on the quality of the obtained clusters was determined. And optimal combinations of clustering methods (K-Means, Agglomerative Hierarchical Clustering and HDBSCAN) and vectorization of tweets were found based on the analysis of 27 clusterings of one data sample. The method of presenting clusters of tweets in a short format is selected. Results. Algorithms using the Levenstein Distance, i.e. fuzz sort, fuzz set and levenshtein, showed the best results. These algorithms quickly perform checks, have a greater difference in similarities, so it is possible to more accurately determine the limit of similarity. According to the results of the clustering, the optimal solutions are to use the HDBSCAN clustering algorithm and the BERT vectorization algorithm to achieve the most accurate results, and to use K-Means together with TF-IDF to achieve the best speed with the optimal result. Stemming can be used to reduce execution time. Conclusions. In this study, the optimal options for comparing cluster fingerprints among the following similarity search methods were experimentally found: Fuzz Sort, Fuzz Set, Levenshtein, Jaro Winkler, Jaccard, Sorensen, Cosine, Sift4. In some algorithms, the average fingerprint similarity reaches above 70%. 3 effective tools were found to compare their similarity, as they show a sufficient difference between comparisons of similar and different clusters (> 20%). Based on the selected effective methods, trend analysis was successfully performed on 90,000 tweets over 7 days for 5 topics of the week using K-Means and TF-IDF for clustering and vectorization, as well as fuzz sort for cluster fingerprint comparison with a 55% similarity threshold.

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Section: матеріали та методиunclassified

Ukrainian Language Tweets Analysis Technology for Public Opinion Dynamics Change Prediction Based on Machine Learning

Prokipchuk¹,

Vysotska²

2023

RIC

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A Comparison of Different Topic Modeling Methods through a Real Case Study of Italian Customer Care

et al. 2023

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The paper deals with the analysis of conversation transcriptions between customers and agents in a call center of a customer care service. The objective is to support the analysis of text transcription of human-to-human conversations, to obtain reports on customer problems and complaints, and on the way an agent has solved them. The aim is to provide customer care service with a high level of efficiency and user satisfaction. To this aim, topic modeling is considered since it facilitates insightful analysis from large documents and datasets, such as a summarization of the main topics and topic characteristics. This paper presents a performance comparison of four topic modeling algorithms: (i) Latent Dirichlet Allocation (LDA); (ii) Non-negative Matrix Factorization (NMF); (iii) Neural-ProdLDA (Neural LDA) and Contextualized Topic Models (CTM). The comparison study is based on a database containing real conversation transcriptions in Italian Natural Language. Experimental results and different topic evaluation metrics are analyzed in this paper to determine the most suitable model for the case study. The gained knowledge can be exploited by practitioners to identify the optimal strategy and to perform and evaluate topic modeling on Italian natural language transcriptions of human-to-human conversations. This work can be an asset for grounding applications of topic modeling and can be inspiring for similar case studies in the domain of customer care quality.

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Data-Driven Identification of German Phrasal Compounds

Cited by 2 publications

References 17 publications

Ukrainian Language Tweets Analysis Technology for Public Opinion Dynamics Change Prediction Based on Machine Learning

Ukrainian Language Tweets Analysis Technology for Public Opinion Dynamics Change Prediction Based on Machine Learning

A Comparison of Different Topic Modeling Methods through a Real Case Study of Italian Customer Care

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