Deep Persian sentiment analysis: Cross-lingual training for low-resource languages

Ghasemi, Rouzbeh; Asli, Seyed Arad Ashrafi; Momtazi, Saeedeh

doi:10.1177/0165551520962781

Cited by 33 publications

(13 citation statements)

References 33 publications

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“…Also, they attempted to address challenges in Persian language by applying DL methods which only achieved the f-score of 55.5%. Ghasemi et al [112] developed a sentiment analysis task in Persian language by proposing a cross-lingual deep learning framework to benefit from available training data of the English language. Deep learning models such as CNN and LSTM and their combinations were experimented with to achieve the fscore of 91.8% on LSTM-CNN.…”

Section: Emerging Msa Areasmentioning

confidence: 99%

Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape

2023

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Sentiment analysis automatically evaluates people's opinions of products or services. It is an emerging research area with promising advancements in high-resource languages such as Indo-European languages (e.g. English). However, the same cannot be said for languages with limited resources. In this study, we evaluate multilingual sentiment analysis (MSA) techniques for under-resourced languages and the use of high-resourced languages to develop resources for MSA in low-resource languages, with the ultimate goal of identifying appropriate strategies for future MSA investigations. We report over 35 studies with different languages demonstrating an interest in developing MSA models for under-resourced languages in a multilingual context. Furthermore, we illustrate the drawbacks of each strategy used for the MSA task. Our focus is critically comparing MSA methods and employed datasets and identifying research gaps. Our comparative analysis study contributes to theoretical literature reviews with complete coverage of MSA studies from 2008 to date. Furthermore, we demonstrate how MSA studies have grown tremendously. Finally, because most studies propose MSA methods based on deep learning approaches, we offer a deep learning framework for MSA that does not rely on machine translation systems. According to the metaanalysis (PRISMA) protocol of this literature review, we found that, in general, just over 60% of the studies have used deep learning frameworks, which significantly improved the MSA performance. Therefore, deep learning methods are recommended for the development of MSA for under-resourced languages.

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Section: Emerging Msa Areasmentioning

confidence: 99%

Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape

2023

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“…At first, two novel deep learning architectures, including bidirectional LSTM and CNN, are proposed, which are designed and can classify sentences in both cases. Then, three data augmentation methods are proposed for the low-resources Persian sentiment corpus [11]. In [22], the authors proposed a transfer learning algorithm based on cellular learning automata (CLA) to reduce negative transfers.…”

Section: Binary Sentiment Classificationmentioning

confidence: 99%

“…The most recent research has been on English and languages that have sufficient resources. Still, limited research has been done in low-source languages such as Persian to identify emotional states, and most of this research has identified positive and negative polarity [11]. In recent years, deep learning has been used to solve many problems related to natural language processing and sentiment analysis of texts, which has performed better than previous methods [44].…”

Section: Related Workmentioning

confidence: 99%

Deep Emotion Detection Sentiment Analysis of Persian Literary Text

Khodaei

Bastanfard

Saboohi

et al. 2022

Preprint

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A large amount of training data is needed to achieve accurate results in processing deep learning models. Although deep learning has played a successful role in sentiment analysis of texts, a new era in natural language processing has begun with the rise of pre-trained language models. Pre-trained models provide access to a large amount of training data. Emotion analysis in Persian text is challenging because, besides being one of the low resource languages, it needs more accurate methods for processing texts. In this study, a dataset that includes the emotional sentences of Persian literary texts was created. We compared two different methods for analyzing the emotion of Persian sentences. The first method uses the FastText and the Bi-LSTM model for emotion classification. In the second approach, we employ the XLM-R based on a deep bidirectional transformer to extract features from text and apply the Catboost algorithm to guide the model to focus on the most relevant class. The proposed model can be used in all languages, especially low-resource languages and imbalanced datasets. According to the experimental results, the proposed model achieved better binary classification results and employing the XLM-R, and Catboost algorithm yielded remarkable improvement in the multi-classification accuracy.

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“…e online social network sentiment analysis and detection model based on user characteristics can improve the accuracy of detection to a certain extent, but at the same time, there are serious problems because normal users will spread the social network sentiment analysis without knowing it, so the misjudgment rate of this method is relatively high. Cue words in the text are constructed and analyzed in an attempt to predict early social network sentiment analysis [17]. For the health-related social network sentiment analysis, the characteristics of information sources of samples were analyzed, such as the URL and site name of the information source website, and then the logistic regression model was used to test the online social network sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

Application of the Deep Pretrained Language Model Processing Method in Social Network Sentiment Analysis

Yana

2022

Mathematical Problems in Engineering

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In social network, users can manage their social network and social identity, publish information on various topics, and obtain information published by other users through friend relationship. The resulting large amount of text data attract more and more scholars to study it. Text sentiment analysis has become a hot spot in social network data analysis and has important application value in academic field, social field, and business field. Based on the idea of pre-training, this paper improves the random word masking algorithm of deep pretraining task in the BERT (Bidirectional Encoder Representation) model to improve the efficiency and stability of model pretraining. Second, a new pretraining task of original sentence judgment is designed to enable the model to measure the degree of sentence smoothness, so that the BERT model can better understand the semantics of context. By referring to the idea of attention mechanism, a deep learning framework with attention weight added into gated convolution is constructed and the special attention weight method is adopted to enhance semantic information. Second, gating convolution and attention mechanism are combined to model aspect-related semantic information and text complete semantic information. Finally, classify the emotion classifier layer of social network, use Softmax function to complete negative, positive, and neutral multiple classifications and calculate the result of emotion classification. By applying the optimized convolutional neural network cyclic optimization network to single task and multitask in practice, the feasibility of applying the optimized convolutional neural network and cyclic neural network to social network sentiment analysis is verified.

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Deep Persian sentiment analysis: Cross-lingual training for low-resource languages

Cited by 33 publications

References 33 publications

Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape

Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape

Deep Emotion Detection Sentiment Analysis of Persian Literary Text

Application of the Deep Pretrained Language Model Processing Method in Social Network Sentiment Analysis

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