Upwards Intensifiers in the English, German and Croatian Language

Pintarić, Anita Pavić; Frleta, Zrinka

doi:10.4312/vestnik.6.31-48

Cited by 9 publications

(15 citation statements)

References 2 publications

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“…Moreover, Glavaš et al in [36] focused on the task of semi-supervised lexicon acquisition, and Pintarić et al in [47] gave insight into intensifiers of the Croatian language. The Serbian language has been the subject of extensive research in the field of computational linguistics, with numerous studies exploring various aspects of language processing.…”

Section: Sentiment Analysis In the Bosnian/croatian/serbian/slovenian...mentioning

confidence: 99%

“…When it comes to intensifiers in the Bosnian, Croatian, Serbian, and Slovenian languages (as languages from the same language group), there has not been much research on the impact of intensifiers and negation on the sentiment value of the text. Moreover, Pintarić and Frleta in [47] give an insight into intensifiers (Maximizers, Boosters, and Moderators) that are in use in Croatian with an emphasis on collocation. However, the intensifiers collocate mostly with adjectives, adverbs, and verbs through the various semantic fields.…”

Section: Intensifiers and Negation In The Bosnian/croatian/serbian/sl...mentioning

confidence: 99%

“…In addition to the methodology described above, we also considered the presence of negation clues and terms from the AnA-words list in our sentiment analysis. We included all 6 types of intensifiers [70,71], or 3 types as described by [47], in one group of Affirmative and non-Affirmative words (AnA-words). Jahić and Vičič in [42,75] pointed out that the list of AnA-words consists of 102 words, mostly adverbs.…”

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confidence: 99%

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Impact of Negation and AnA-Words on Overall Sentiment Value of the Text Written in the Bosnian Language

Jahić

Vičič

2023

Applied Sciences

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In this manuscript, we present our efforts to develop an accurate sentiment analysis model for Bosnian-language tweets which incorporated three elements: negation cues, AnA-words (referring to maximizers, boosters, approximators, relative intensifiers, diminishers, and minimizers), and sentiment-labeled words from a lexicon. We used several machine-learning techniques, including SVM, Naive Bayes, RF, and CNN, with different input parameters, such as batch size, number of convolution layers, and type of convolution layers. In addition to these techniques, BOSentiment is used to provide an initial sentiment value for each tweet, which is then used as input for CNN. Our best-performing model, which combined BOSentiment and CNN with 256 filters and a size of 4×4, with a batch size of 10, achieved an accuracy of over 92%. Our results demonstrate the effectiveness of our approach in accurately classifying the sentiment of Bosnian tweets using machine-learning techniques, lexicons, and pre-trained models. This study makes a significant contribution to the field of sentiment analysis for under-researched languages such as Bosnian, and our approach could be extended to other languages and social media platforms to gain insight into public opinion.

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Section: Sentiment Analysis In the Bosnian/croatian/serbian/slovenian...mentioning

confidence: 99%

Section: Intensifiers and Negation In The Bosnian/croatian/serbian/sl...mentioning

confidence: 99%

mentioning

confidence: 99%

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Impact of Negation and AnA-Words on Overall Sentiment Value of the Text Written in the Bosnian Language

Jahić

Vičič

2023

Applied Sciences

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“…First, the students were asked to define the term 'intensifiers', which was done correctly by 37 students (47%). Then, they were given the list of English and Croatian intensifiers determined in Pavić Pintarić and Frleta (2014). Students were first given the list of English intensifiers (completely, utterly, awfully, really, very, greatly, thoroughly, madly, perfectly, immensely, highly, grievously, horribly, extremely, deeply, deadly, utterly, furiously, wildly, madly, absolutely) and asked to circle the ones they knew.…”

Section: Questionnairementioning

confidence: 99%

Transfer in the use of intensifiers

Lovrović

Pintarić

2019

vestnik

Self Cite

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This paper investigates transfer in the use of English intensifiers by Croatian students of English. Since transfer is an important factor in second language acquisition and is very common in different areas of language use, it has been the subject of various studies. In this paper, we focus on lexical transfer investigating its effects on the use of intensifiers among first-year undergraduate university students of English at Zadar University, Croatia. The aim of this cross-sectional study is to examine whether there is a connection between the use of intensifiers in L2 and L1, i.e. whether the knowledge of intensifiers in L1 Croatian influences learners' use or knowledge of intensifiers in L2 English. Intensity is a basic human cognitive category and has an important function in communication. Specifically, the speaker expresses emotions or an attitude towards a topic using intensifiers. Furthermore, intensifiers are used in different collocations modifying adjectives, adverbs, and verbs. However, this study focuses only on intensifiers that appear in adverb-adjective collocations, and their use is observed in a relatively large group of language learners at a single point in time. Relevant data are collected using questionnaires as well as cloze-tests and translation tasks which focus on semantics and collocation.

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“…For the lexicon, we utilized the KSS 1.1 Slovene opinion lexicon, which contains 5125 negative lemmas and 1911 positive lemmas which is described in sections 4 and 5 and subsection 5.1. As for the AnAwords, we used lists provided by Patra et al [4] , Pintarić et al [5] ., and by Osmankadić [6] , more in subsection 5.2. For the stopwords, we employed the Croatian stopwords list provided by the SpaCy package in Python, which were extended and described in section 5 and subsection 5.3.…”

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confidence: 99%