Creating a stop word dictionary in Serbian

Marovac, Ulfeta; Avdić, A.M.; Ljajić, Adela

doi:10.5937/spsunp2101017m

Cited by 4 publications

(2 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We removed the URLs, mentions, etc, using regular expressions. We used the list of stop words described by Marovac et al [ 61 ], which we extended with all the alternative names for COVID-19 and derivatives of the word “vaccine.” These terms naturally appear in most tweets since we applied them as our Twitter search keywords.…”

Section: Methodsmentioning

confidence: 99%

Uncovering the Reasons Behind COVID-19 Vaccine Hesitancy in Serbia: Sentiment-Based Topic Modeling

Ljajić¹,

Prodanović²,

Medvecki³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

Background Since the first COVID-19 vaccine appeared, there has been a growing tendency to automatically determine public attitudes toward it. In particular, it was important to find the reasons for vaccine hesitancy, since it was directly correlated with pandemic protraction. Natural language processing (NLP) and public health researchers have turned to social media (eg, Twitter, Reddit, and Facebook) for user-created content from which they can gauge public opinion on vaccination. To automatically process such content, they use a number of NLP techniques, most notably topic modeling. Topic modeling enables the automatic uncovering and grouping of hidden topics in the text. When applied to content that expresses a negative sentiment toward vaccination, it can give direct insight into the reasons for vaccine hesitancy. Objective This study applies NLP methods to classify vaccination-related tweets by sentiment polarity and uncover the reasons for vaccine hesitancy among the negative tweets in the Serbian language. Methods To study the attitudes and beliefs behind vaccine hesitancy, we collected 2 batches of tweets that mention some aspects of COVID-19 vaccination. The first batch of 8817 tweets was manually annotated as either relevant or irrelevant regarding the COVID-19 vaccination sentiment, and then the relevant tweets were annotated as positive, negative, or neutral. We used the annotated tweets to train a sequential bidirectional encoder representations from transformers (BERT)-based classifier for 2 tweet classification tasks to augment this initial data set. The first classifier distinguished between relevant and irrelevant tweets. The second classifier used the relevant tweets and classified them as negative, positive, or neutral. This sequential classifier was used to annotate the second batch of tweets. The combined data sets resulted in 3286 tweets with a negative sentiment: 1770 (53.9%) from the manually annotated data set and 1516 (46.1%) as a result of automatic classification. Topic modeling methods (latent Dirichlet allocation [LDA] and nonnegative matrix factorization [NMF]) were applied using the 3286 preprocessed tweets to detect the reasons for vaccine hesitancy. Results The relevance classifier achieved an F-score of 0.91 and 0.96 for relevant and irrelevant tweets, respectively. The sentiment polarity classifier achieved an F-score of 0.87, 0.85, and 0.85 for negative, neutral, and positive sentiments, respectively. By summarizing the topics obtained in both models, we extracted 5 main groups of reasons for vaccine hesitancy: concern over vaccine side effects, concern over vaccine effectiveness, concern over insufficiently tested vaccines, mistrust of authorities, and conspiracy theories. Conclusions This paper presents a combination of NLP methods applied to find the reasons for vaccine hesitancy in Serbia. Given these reasons, it is now possible to better understand the concerns of people regarding the vaccination process.

show abstract

Section: Methodsmentioning

confidence: 99%

Uncovering the Reasons Behind COVID-19 Vaccine Hesitancy in Serbia: Sentiment-Based Topic Modeling

Ljajić¹,

Prodanović²,

Medvecki³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…The file contains two columns: word and label. The label describes the type of words: auxiliary verbs (V), pronouns (PRON), adverbs (ADV), prepositions (PREP), conjunctions (CONJ), exclamations (EXCL), particles (PART) and abbreviations (ABBR) (Marovac et al, 2021).…”

Section: Dictionaries and Terminologiesmentioning

confidence: 99%

A Survey of Resources and Methods for Natural Language Processing of Serbian Language

Marovac

Avdić

Milošević

2023

Preprint

View full text Add to dashboard Cite

The Serbian language is a Slavic language spoken by over 12 million speakers and well understood by over 15 million people. In the area of natural language processing, it can be considered a low-resourced language. Also, Serbian is considered a high-inflectional language. The combination of many word inflections and low availability of language resources makes natural language processing of Serbian challenging. Nevertheless, over the past three decades, there have been a number of initiatives to develop resources and methods for natural language processing of Serbian, ranging from developing a corpus of free text from books and the internet, annotated corpora for classification and named entity recognition tasks to various methods and models performing these tasks. In this paper, we review the initiatives, resources, methods, and their availability.

show abstract

Outlier Detection in Serbian CommonCrawl Data

Kalušev,

Ćulibrk

2024

2024 23rd International Symposium INFOTEH-JAHORINA (INFOTEH)

View full text Add to dashboard Cite

Creating a stop word dictionary in Serbian

Cited by 4 publications

References 72 publications

Uncovering the Reasons Behind COVID-19 Vaccine Hesitancy in Serbia: Sentiment-Based Topic Modeling

Uncovering the Reasons Behind COVID-19 Vaccine Hesitancy in Serbia: Sentiment-Based Topic Modeling

A Survey of Resources and Methods for Natural Language Processing of Serbian Language

Outlier Detection in Serbian CommonCrawl Data

Contact Info

Product

Resources

About