Change of Word Characteristics in 20th-Century Turkish Literature: A Statistical Analysis

Can, Fazlı; Patton, Jon

doi:10.1080/09296174.2010.485444

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…A similar task gives successful results when applied to the written texts. For example, out of 40 Turkish novels, the gender of writers is classified correctly with an accuracy of 88.23%, while female authors classified with 100% accuracy (Can & Patton, 2010). In the same study, the era of the novel, in which it is written, rather than the age of the authors, is also predicted with 57.27% accuracy, out of 4 eras, where each is composed of 25 years.…”

Section: Rq1: Predicting Age and Gender Of The Participantmentioning

confidence: 84%

Quantitative Analysis of Spoken Discourse Using Memoirs of Old-time Moviegoers

Çalışkan

Can

Akbulut

et al. 2021

Journal of Quantitative Linguistics

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We present the first quantitative analysis of spoken discourse for the Turkish language using memoirs of a group of old-time moviegoers of varying age groups whose birth year spreads over a period of four decades ranging from the 1930s to the 1960s. They tell their experiences by answering a set of questions. Their responses are evaluated comprehensively with the expectation that various attributes of the participants are reflected by their everyday speaking language. We also investigate their language characteristics in terms of their vocabulary richness and word usage. The results show that the age and gender of the participants can be inferred to some extent from their speech, as is the case for written text. However, the difference is not significant in the language use of younger and older respondents in terms of vocabulary richness and archaic word usage. With additional data obtained for some participants, it is shown that text can be accurately identified as being either spoken or written; however, the spoken text of a person can only be differentiated from their written text with the accuracy level of a random guess.

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Section: Rq1: Predicting Age and Gender Of The Participantmentioning

confidence: 84%

Quantitative Analysis of Spoken Discourse Using Memoirs of Old-time Moviegoers

Çalışkan

Can

Akbulut

et al. 2021

Journal of Quantitative Linguistics

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“…Can and Patton used word length and most frequent word as features to analyze the change in writing style with time for Turkish language literature [18]. In another study, in 2010, Can and Patton analyzed 20th‐century Turkish literature in terms of the change in word characteristics [19]. Kao and Jurafsky [20] used stylistic features to classify poems as written by professional and amateur poets.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic extraction of similar poetry for study of literary texts: An experiment on Hindi poetry

2022

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The study of literary texts is one of the earliest disciplines practiced around the globe. Poetry is artistic writing in which words are carefully chosen and arranged for their meaning, sound, and rhythm. Poetry usually has a broad and profound sense that makes it difficult to be interpreted even by humans. The essence of poetry is Rasa, which signifies mood or emotion. In this paper, we propose a poetry classification‐based approach to automatically extract similar poems from a repository. Specifically, we perform a novel Rasa‐based classification of Hindi poetry. For the task, we primarily used lexical features in a bag‐of‐words model trained using the support vector machine classifier. In the model, we employed Hindi WordNet, Latent Semantic Indexing, and Word2Vec‐based neural word embedding. To extract the rich feature vectors, we prepared a repository containing 37 717 poems collected from various sources. We evaluated the performance of the system on a manually constructed dataset containing 945 Hindi poems. Experimental results demonstrated that the proposed model attained satisfactory performance.

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“…They obtained accuracies of 82.2% in prediction of gender using SVM with a term-based feature set. In [26], they considered the author gender identification problem using discriminant analysis and analyzed the change of frequent word usage with gender. The authors of [27] presented an n-gram model for identifying the gender of authors.…”

Section: Related Workmentioning

confidence: 99%

A comparative study of author gender identification

Yıldız¹

2019

Turk J Elec Eng & Comp Sci

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In recent years, author gender identification has gained considerable attention in the fields of information retrieval and computational linguistics. In this paper, we employ and evaluate different learning approaches based on machine learning (ML) and neural network language models to address the problem of author gender identification. First, several ML classifiers are applied to the features obtained by bag-of-words. Secondly, datasets are represented by a low-dimensional real-valued vector using Word2vec, GloVe, and Doc2vec, which are on par with ML classifiers in terms of accuracy. Lastly, neural networks architectures, the convolution neural network and recurrent neural network, are trained and their associated performances are assessed. A variety of experiments are successfully conducted. Different issues, such as the effects of the number of dimensions, training architecture type, and corpus size, are considered. The main contribution of the study is to identify author gender by applying word embeddings and deep learning architectures to the Turkish language.

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Change of Word Characteristics in 20th-Century Turkish Literature: A Statistical Analysis

Cited by 11 publications

References 31 publications

Quantitative Analysis of Spoken Discourse Using Memoirs of Old-time Moviegoers

Quantitative Analysis of Spoken Discourse Using Memoirs of Old-time Moviegoers

Automatic extraction of similar poetry for study of literary texts: An experiment on Hindi poetry

A comparative study of author gender identification

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