A Survey on Sentiment Analysis in Persian: a Comprehensive System Perspective Covering Challenges and Advances in Resources and Methods

Rajabi, Zeinab; Valavi, MohammadReza

doi:10.1007/s12559-021-09886-x

Cited by 9 publications

(3 citation statements)

References 53 publications

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“…We use a rule-based approach to detect negation for sentiment analysis that largely draws information from lexicons. In this approach, every verb with the particle "ن ", "نا ", "ضد " (important shifters) is considered as negated [ 20 ].…”

Section: Methodsmentioning

confidence: 99%

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Use of sentiment analysis for capturing hospitalized cancer patients' experience from free-text comments in the Persian language

Yazdani,

Shamloo,

Khaki

et al. 2023

BMC Med Inform Decis Mak

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Purpose Today, the Internet provides access to many patients' experiences, which is crucial in assessing the quality of healthcare services. This paper introduces a model for detecting cancer patients' opinions about healthcare services in the Persian language, both positive and negative. Method To achieve the objectives of this study, a combination of sentiment analysis (SA) and topic modeling approaches was employed. All pertinent comments made by cancer patients were collected from the patient feedback form of the Tehran University of Medical Science (TUMS) Cancer Institute (CI) in Iran, from March to October 2021. Conventional evaluation metrics such as accuracy, precision, recall, and F-measure were utilized to assess the performance of the proposed model. Result The experimental findings revealed that the proposed SA model achieved accuracies of 89.3%, 92.6%, and 90.8% in detecting patients' sentiments towards general services, healthcare services, and life expectancy, respectively. Based on the topic modeling results, the topic "Metastasis" exhibited lower sentiment scores compared to other topics. Additionally, cancer patients expressed dissatisfaction with the current appointment booking service, while topics such as "Good experience," "Affable staff", and "Chemotherapy" garnered higher sentiment scores. Conclusion The combined use of SA and topic modeling offers valuable insights into healthcare services. Policymakers can utilize the knowledge obtained from these topics and associated sentiments to enhance patient satisfaction with cancer institution services.

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Section: Methodsmentioning

confidence: 99%

“…SA in every language has specific prerequisites. Therefore, the direct use of methods, tools, and resources developed for the English language in Persian has limitations [ 20 ]. Therefore, it seems necessary to analyze patients' emotions in the Persian Language.…”

Section: Introductionmentioning

confidence: 99%

Use of sentiment analysis for capturing hospitalized cancer patients' experience from free-text comments in the Persian language

Yazdani,

Shamloo,

Khaki

et al. 2023

BMC Med Inform Decis Mak

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“…Nevertheless, over time, many researchers have started SA tasks in lowresource languages, having recognised the need to identify the sentiment for text written in languages that are not the mostly used ones. Approaches for several low-resources languages of the Asian geographic area have recently been studied by several authors like El-Haj et al [36] for the Arabic language, Le et al [37] for Indonesian, Rajabi et al [38] on the Persian language, and Gangula and Mamidi [39] for Telugu language, which is one of the six languages designated as a classical language by the Government of India and the 14th most-spoken native language in the world. Nasib et al [40] in 2018 obtained good accuracy results converting natural Bengali language to text with an adequately trained model based on an open-source text-to-speech Java framework.…”

Section: Related Workmentioning

confidence: 99%

Revealing People’s Sentiment in Natural Italian Language Sentences

Calvagna,

Tramontana,

Verga

2023

Computers

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Social network systems are constantly fed with text messages. While this enables rapid communication and global awareness, some messages could be aptly made to hurt or mislead. Automatically identifying meaningful parts of a sentence, such as, e.g., positive or negative sentiments in a phrase, would give valuable support for automatically flagging hateful messages, propaganda, etc. Many existing approaches concerned with the study of people’s opinions, attitudes and emotions and based on machine learning require an extensive labelled dataset and provide results that are not very decisive in many circumstances due to the complexity of the language structure and the fuzziness inherent in most of the techniques adopted. This paper proposes a deterministic approach that automatically identifies people’s sentiments at the sentence level. The approach is based on text analysis rules that are manually derived from the way Italian grammar works. Such rules are embedded in finite-state automata and then expressed in a way that facilitates checking unstructured Italian text. A few grammar rules suffice to analyse an ample amount of correctly formed text. We have developed a tool that has validated the proposed approach by analysing several hundreds of sentences gathered from social media: hence, they are actual comments given by users. Such a tool exploits parallel execution to make it ready to process many thousands of sentences in a fraction of a second. Our approach outperforms a well-known previous approach in terms of precision.

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A comprehensive survey on deep learning-based approaches for multimodal sentiment analysis

Ghorbanali

Sohrabi

2023

Artif Intell Rev

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A Survey on Sentiment Analysis in Persian: a Comprehensive System Perspective Covering Challenges and Advances in Resources and Methods

Cited by 9 publications

References 53 publications

Use of sentiment analysis for capturing hospitalized cancer patients' experience from free-text comments in the Persian language

Use of sentiment analysis for capturing hospitalized cancer patients' experience from free-text comments in the Persian language

Revealing People’s Sentiment in Natural Italian Language Sentences

A comprehensive survey on deep learning-based approaches for multimodal sentiment analysis

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