Application of text mining technologies in Russian language for solving the problems of primary financial monitoring

Victor, Radygin; Kupriyanov, D. Yu.; Bessonov, R.A.; Ivanov, M.N.; Osliakova, I.V.

doi:10.1016/j.procs.2021.06.078

Cited by 7 publications

(3 citation statements)

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“…The authors note that an essential element in such studies is the identification of many aspects of views, moods, and emotions, which contributes to improving the accuracy of the assessment. Radygin et al (2021) study the application of intellectual analysis to Russian-language texts. The authors offer a set of programs for a narrow subject area (violations of the Federal Procurement Law).…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of Text Mining in Socio-Economic Research

Malyshenko,

Anashkina

et al. 2024

International Journal of Business Data Communications and Networking

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This work aims to analyze insights from social networks for identification of population satisfaction with pay level in Russia using the text mining approach. For this, a sentiment analysis framework was developed, which integrates Twitter mining tools and a sentiment index. Sentiments were extracted using Twitter mining and then recoded and substituted into the sentiment formula. The results of sentiment analysis indicate low satisfaction with levels of pay among Russians. Twitter was chosen as the object of research, as one of the most active and independent networks in Russia. It is possible that some of the tweets belong to authors who are not living in Russia at the moment, but their number is not significant and their interest in this issue, in the authors' opinion, only enhances the relevance of the problem under study.

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Section: Literature Reviewmentioning

confidence: 99%

Implementation of Text Mining in Socio-Economic Research

Malyshenko,

Anashkina

et al. 2024

International Journal of Business Data Communications and Networking

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“…The first stage in preprocessing is tokenizing [12]. Several processes are carried out at this stage: lowercasing, punctuation removal, and breaking a sentence into separate words [14]. The process can be seen in Table II.…”

Section: ) Tokenizingmentioning

confidence: 99%

Indonesian Society’s Sentiment Analysis Against the COVID-19 Booster Vaccine

Dionisia Bhisetya Rarasati,

Angelina Pramana Thenata,

Afiyah Salsabila Arief

2023

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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The COVID-19 pandemic is still occurring in various countries, including Indonesia. This pandemic is caused by the coronavirus, which has mutated into multiple virus variants, such as Delta and Omicron. As of 9 February 2022, 4,626,936 people were confirmed positive for COVID-19 in Indonesia. This number continues to rise. The Indonesian government has prevented the spread of these virus variants by introducing booster vaccines to the public. However, this vaccination program has caused various sentiments among Indonesians. To optimize efforts to combat COVID-19, the government needs to know these sentiments immediately. Based on these problems, the researcher proposes the application of machine learning technology to develop a system that can analyze the sentiments of the Indonesians toward the booster vaccine. This research has several stages: data collection, data labeling, text preprocessing, feature extraction, and application of the support vector machine (SVM) algorithm using various kernels, namely the linear kernel, Gaussian radial basis function (RBF) kernel, and polynomial kernel. Furthermore, the results of the system were tested for accuracy using a 10-fold cross validation and confusion matrix. The dataset used was 681 tweets with the hashtag “vaksinbooster.” The dataset consists of two classes: negative (0) and positive (1). The results showed that the data were positive for the booster vaccine, as evidenced by the higher number of positive tweets, with 554 data, compared to 127 negative tweets. In addition, the dataset was divided into training data of 545 and testing data of 136. In addition, the test results of this study revealed that the SVM algorithm with the polynomial kernel, which was evaluated with 10-fold cross validation, yielded the highest level of accuracy, namely 79.22%.

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“…Text analytics, sometimes called text mining, is the integration of linguistic, computational statistics, and computer science techniques [3]. The research on text analytics has been conducted in a variety of languages, including English [4], Arabic [5], Russian [6], Indian [7], Chinese [8], and Thailand [9] to solve various problems in text classification, text clustering, sentiment analysis, and topic modeling. Text analytics have a significant impact on data science [10], [11].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Stemming Techniques on the Malay Text

Mohemad,

Muhait,

Noor

et al. 2023

IJACSA

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Text stemming, an essential preprocessing step in the development of Natural Language Processing (NLP) applications, involves the transformation of various word forms into their root words. Stemming plays a critical role in decreasing the volume of text, thereby enhancing the efficiency of various computational tasks such as information retrieval, text classification, and text clustering. Stemming is a rule-based approach. On the other hand, it frequently suffers affixation errors that result in under-stemming, over-stemming, or both, as well as unstemmed or spelling exceptions. Every language has different stemming techniques, and among the most well-known Malay stemming algorithms are the Othman and Ahmad algorithms. Therefore, this study aims to compare the performance of the stemming errors between the Othman and Ahmad algorithms in stemming Malay text, particularly on two different domains of textual datasets, which are the course summaries of the education domain and housebreaking crime reports of the crime domain. The Othman algorithm presents a set of 121 stemming rules (set A). In the meantime, Ahmad's algorithm proposes two distinct sets of stemming rules, comprising 432 (set B) and 561 rules (set C), respectively. Based on the experiment results with 100 course summaries, the Ahmad algorithm (Set B) obtained a higher accuracy rate of 93.61%. The second highest is the Ahmad algorithm (Set C) with 93.53%. The Othman algorithm achieved the lowest accuracy with 86.04% compared to the other two algorithms. Meanwhile, findings from the experiment with 100 housebreaking crime reports show similar results, with the Ahmad algorithm (Set C) achieving the highest stemming accuracy of approximately 93.80% and the Othman algorithm producing the lowest stemming accuracy (83.09%). The result indicates that stemming accuracy is consistent across different types of datasets.

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Application of text mining technologies in Russian language for solving the problems of primary financial monitoring

Cited by 7 publications

References 10 publications

Implementation of Text Mining in Socio-Economic Research

Implementation of Text Mining in Socio-Economic Research

Indonesian Society’s Sentiment Analysis Against the COVID-19 Booster Vaccine

A Comparative Study of Stemming Techniques on the Malay Text

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