Persian text classification based on topic models

Ahmadi, Parvin; Tabandeh, Mahmoud; Gholampour, Iman

doi:10.1109/iraniancee.2016.7585495

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…This gap in available resources has motivated researchers like Hosseini et al to concentrate on measuring and tracking the evolution of the pandemic response using Persian tweets [33]. In a similar vein, Parvin Ahmadi et al proposed a method for Persian text classification, wherein topic models were utilized as a means of classifying Persian texts [34].…”

Section: Low-resource Languagesmentioning

confidence: 99%

Comparative Analysis of Topic Modeling Algorithms for Short Texts in Persian Tweets

Karimi

Akbari

2023

Preprint

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Topic modeling is a popular natural language processing technique to uncover hidden patterns and topics in extensive text collections. However, there is a lack of comprehensive studies that focus specifically on applying topic modeling algorithms to short texts, particularly from social media platforms. Even fewer studies have explored comparing different topic modeling algorithms for low-resource languages such as Persian. Our study aims to address this gap by thoroughly investigating topic modeling algorithms and metrics tailored for short texts, particularly Persian tweets. We collected and preprocessed a substantial dataset of Persian tweets. We also developed a dedicated tool that enables reproducibility and facilitates the evaluation of various topic modeling algorithms using the provided dataset. Our comparative analysis included Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), Latent Semantic Indexing (LSI), Gibbs Sampling Dirichlet Mixture Model (GSDMM), and Correlated Topic Model (CTM). To measure their performance, we employed well-established metrics, namely Purity, Normalized Mutual Information (NMI), and Coherence. Our experimental results indicate that GSDMM and CTM+BERT exhibit superior performance compared to other algorithms in terms of purity and NMI on the Persian short-text topic modeling dataset. Additionally, CTM+BERT demonstrates competitive coherence performance compared to GSDMM. Our study provides valuable insights into the effectiveness of different topic modeling approaches for short texts and can help researchers select the most appropriate algorithm for their specific use case.

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Section: Low-resource Languagesmentioning

confidence: 99%

Comparative Analysis of Topic Modeling Algorithms for Short Texts in Persian Tweets

Karimi

Akbari

2023

Preprint

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“…Among text classification models introduced in Persian, the topic-model approach (Ahmadi, Tabandeh, and Gholampour 2016) overcame the problems of dealing with bag of words, which considered each token as a feature, thus dealing with a vast number of elements and features inside a document. Besides, (Moradi and Bahrani 2016) narrowed the task of text classification down to gender domain, where different statistical models such as Naïve Bayes, alternating decision tree and support vector machine were evaluated.…”

Section: Related Workmentioning

confidence: 99%

PGST: A Persian gender style transfer method

Khanmohammadi

Mirroshandel

2023

Nat. Lang. Eng.

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Recent developments in text style transfer have led this field to be more highlighted than ever. There are many challenges associated with transferring the style of input text such as fluency and content preservation that need to be addressed. In this research, we present PGST, a novel Persian text style transfer approach in the gender domain, composed of different constituent elements. Established on the significance of parts of speech tags, our method is the first that successfully transfers the gendered linguistic style of Persian text. We have proceeded with a pre-trained word embedding for token replacement purposes, a character-based token classifier for gender exchange purposes, and a beam search algorithm for extracting the most fluent combination. Since different approaches are introduced in our research, we determine a trade-off value for evaluating different models’ success in faking our gender identification model with transferred text. Our research focuses primarily on Persian, but since there is no Persian baseline available, we applied our method to a highly studied gender-tagged English corpus and compared it to state-of-the-art English variants to demonstrate its applicability. Our final approach successfully defeated English and Persian gender identification models by 45.6% and 39.2%, respectively.

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“…In general, the algorithms that have right classes during their training in their matrix are called supervised learners. The following section addresses the Bayesian algorithm and the support vector machine (SVM) method [16][17][18][19][20].…”

Section: Classificationmentioning

confidence: 99%

Persian Text Classification using naive Bayes algorithms and Support Vector Machine algorithm

Rezaeian

Novikova

2020

IJEEI

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One of the several benefits of text classification is to automatically assign document in predefined category is one of the primary steps toward knowledge extraction from the raw textual data. In such tasks, words are dealt with as a set of features. Due to high dimensionality and sparseness of feature vector results from traditional feature selection methods, most of the proposed text classification methods for this purpose lack performance and accuracy. Many algorithms have been implemented to the problem of Automatic Text Categorization that's why, we tried to use new methods like Information Extraction, Natural Language Processing, and Machine Learning. This paper proposes an innovative approach to improve the classification performance of the Persian text. Naive Bayes classifiers which are widely used for text classification in machine learning are based on the conditional probability. we have compared the Gaussian, Multinomial and Bernoulli methods of naive Bayes algorithms with SVM algorithm. for statistical text representation, TF and TF-IDF and character-level 3 (3-Gram) [1,2] were used. Finally, experimental results on 10 newsgroups.

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Persian text classification based on topic models

Cited by 6 publications

References 14 publications

Comparative Analysis of Topic Modeling Algorithms for Short Texts in Persian Tweets

Comparative Analysis of Topic Modeling Algorithms for Short Texts in Persian Tweets

PGST: A Persian gender style transfer method

Persian Text Classification using naive Bayes algorithms and Support Vector Machine algorithm

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