Ontology-Based Sentiment Analysis of Kazakh Sentences

Yergesh, Banu; Bekmanova, Gulmira; Sharipbay, Altynbek; Yergesh, Manas

doi:10.1007/978-3-319-62398-6_47

Cited by 20 publications

(10 citation statements)

References 10 publications

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“…The methods for creating basic sentences in Kazakh were laid out in [23][24][25][26][27]. It was feasible to automate text parsing using these models [18,28] by using Chomsky's context-free grammar (CFG) to formalize the syntax of uncomplicated sentences in the Kazakh language and create ontological models of these rules within the Protégé environment. This was done to facilitate syntax analysis, considering the semantic aspects of the constituent elements.…”

Section: Related Workmentioning

confidence: 99%

Continuous Sign Language Recognition and Its Translation into Intonation-Colored Speech

Amangeldy

Ukenova

Bekmanova

et al. 2023

Sensors

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This article is devoted to solving the problem of converting sign language into a consistent text with intonation markup for subsequent voice synthesis of sign phrases by speech with intonation. The paper proposes an improved method of continuous recognition of sign language, the results of which are transmitted to a natural language processor based on analyzers of morphology, syntax, and semantics of the Kazakh language, including morphological inflection and the construction of an intonation model of simple sentences. This approach has significant practical and social significance, as it can lead to the development of technologies that will help people with disabilities to communicate and improve their quality of life. As a result of the cross-validation of the model, we obtained an average test accuracy of 0.97 and an average val_accuracy of 0.90 for model evaluation. We also identified 20 sentence structures of the Kazakh language with their intonational model.

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Section: Related Workmentioning

confidence: 99%

Continuous Sign Language Recognition and Its Translation into Intonation-Colored Speech

Amangeldy

Ukenova

Bekmanova

et al. 2023

Sensors

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“…Some works on Uyghur and Kazakh text classification have been reported in [4,9,10]. Tuerxun et al [4] used KNN (K-Nearest Neighbor) as a classifier on Uyghur text to conduct text classification, and used the TFIDF (Term Frequency-Inverse Document Frequency) algorithm to calculate the feature weight in this paper.…”

Section: Stemmentioning

confidence: 99%

“…Imam et al [9] used the TextRank algorithm to select the features to make a sentiment classification experiment on Uyghur text, and SVM (Support Vector Machine) was used as a classifier in this experiment. Yergesh et al [10] made a sentiment classification experiment on Kazakh text based on linguistic rules of Kazakh. The text classification methods used by the researchers mentioned above are the traditional classification framework in which the machine learning process is shallow and do not consider the context relationship between the words in the text or just based on the simple rules, so they are problematic for noisy text.…”

Section: Stemmentioning

confidence: 99%

A Robust Morpheme Sequence and Convolutional Neural Network-Based Uyghur and Kazakh Short Text Classification

2019

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In this paper, based on the multilingual morphological analyzer, we researched the similar low-resource languages, Uyghur and Kazakh, short text classification. Generally, the online linguistic resources of these languages are noisy. So a preprocessing is necessary and can significantly improve the accuracy. Uyghur and Kazakh are the languages with derivational morphology, in which words are coined by stems concatenated with suffixes. Usually, terms are used as the representation of text content while excluding functional parts as stop words in these languages. By extracting stems we can collect necessary terms and exclude stop words. Morpheme segmentation tool can split text into morphemes with 95% high reliability. After preparing both word- and morpheme-based training text corpora, we apply convolutional neural network (CNN) as a feature selection and text classification algorithm to perform text classification tasks. Experimental results show that the morpheme-based approach outperformed the word-based approach. Word embedding technique is frequently used in text representation both in the framework of neural networks and as a value expression, and can map language units into a sequential vector space based on context, and it is a natural way to extract and predict out-of-vocabulary (OOV) from context information. Multilingual morphological analysis has provided a convenient way for processing tasks of low resource languages like Uyghur and Kazakh.

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“…For the speech-based modality, they fine-tuned the CNN-14 of the PANNs framework, and for facial emotion recognizers, they proposed a framework that consists of a pre-trained Spatial Transformer Network on saliency maps and facial images followed by a bi-LSTM with an attention mechanism. Results on sentiment analysis and emotion recognition in the Kazakh language are published in [40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Emotional Speech Recognition Method Based on Word Transcription

Bekmanova

Yergesh

Sharipbay

et al. 2022

Sensors

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The emotional speech recognition method presented in this article was applied to recognize the emotions of students during online exams in distance learning due to COVID-19. The purpose of this method is to recognize emotions in spoken speech through the knowledge base of emotionally charged words, which are stored as a code book. The method analyzes human speech for the presence of emotions. To assess the quality of the method, an experiment was conducted for 420 audio recordings. The accuracy of the proposed method is 79.7% for the Kazakh language. The method can be used for different languages and consists of the following tasks: capturing a signal, detecting speech in it, recognizing speech words in a simplified transcription, determining word boundaries, comparing a simplified transcription with a code book, and constructing a hypothesis about the degree of speech emotionality. In case of the presence of emotions, there occurs complete recognition of words and definitions of emotions in speech. The advantage of this method is the possibility of its widespread use since it is not demanding on computational resources. The described method can be applied when there is a need to recognize positive and negative emotions in a crowd, in public transport, schools, universities, etc. The experiment carried out has shown the effectiveness of this method. The results obtained will make it possible in the future to develop devices that begin to record and recognize a speech signal, for example, in the case of detecting negative emotions in sounding speech and, if necessary, transmitting a message about potential threats or riots.

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Ontology-Based Sentiment Analysis of Kazakh Sentences

Cited by 20 publications

References 10 publications

Continuous Sign Language Recognition and Its Translation into Intonation-Colored Speech

Continuous Sign Language Recognition and Its Translation into Intonation-Colored Speech

A Robust Morpheme Sequence and Convolutional Neural Network-Based Uyghur and Kazakh Short Text Classification

Emotional Speech Recognition Method Based on Word Transcription

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