Producing An Instagram Dataset For Persian Language Sentiment Analysis Using Crowdsourcing Method

Mahsa, Heidari; Shamsinejad, Pirooz

doi:10.1109/icwr49608.2020.9122270

Cited by 9 publications

(4 citation statements)

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“…This system allows working with texts in the Kazakh and Russian languages. It also has built-in modules for connecting to the application programming interfaces (APIs) of social networks: Vkontakte [52], Facebook [53,54], Twitter [22,55], Instagram [56,57], YouTube [58], Telegram [59], and Odnoklassniki [60]. The OMSystem automatically determines the language of the text (Russian, Kazakh) and the sentiment of the topic, as negative, positive, or neutral, using a sentiment dictionary and ML algorithms.…”

Section: The Omsystem Information System Design Methodologymentioning

confidence: 99%

On the development of an information system for monitoring user opinion and its role for the public

et al. 2022

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Social media services and analytics platforms are rapidly growing. A large number of various events happen mostly every day, and the role of social media monitoring tools is also increasing. Social networks are widely used for managing and promoting brands and different services. Thus, most popular social analytics platforms aim for business purposes while monitoring various social, economic, and political problems remains underrepresented and not covered by thorough research. Moreover, most of them focus on resource-rich languages such as the English language, whereas texts and comments in other low-resource languages, such as the Russian and Kazakh languages in social media, are not represented well enough. So, this work is devoted to developing and applying the information system called the OMSystem for analyzing users’ opinions on news portals, blogs, and social networks in Kazakhstan. The system uses sentiment dictionaries of the Russian and Kazakh languages and machine learning algorithms to determine the sentiment of social media texts. The whole structure and functionalities of the system are also presented. The experimental part is devoted to building machine learning models for sentiment analysis on the Russian and Kazakh datasets. Then the performance of the models is evaluated with accuracy, precision, recall, and F1-score metrics. The models with the highest scores are selected for implementation in the OMSystem. Then the OMSystem’s social analytics module is used to thoroughly analyze the healthcare, political and social aspects of the most relevant topics connected with the vaccination against the coronavirus disease. The analysis allowed us to discover the public social mood in the cities of Almaty and Nur-Sultan and other large regional cities of Kazakhstan. The system’s study included two extensive periods: 10-01-2021 to 30-05-2021 and 01-07-2021 to 12-08-2021. In the obtained results, people’s moods and attitudes to the Government’s policies and actions were studied by such social network indicators as the level of topic discussion activity in society, the level of interest in the topic in society, and the mood level of society. These indicators calculated by the OMSystem allowed careful identification of alarming factors of the public (negative attitude to the government regulations, vaccination policies, trust in vaccination, etc.) and assessment of the social mood.

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Section: The Omsystem Information System Design Methodologymentioning

confidence: 99%

On the development of an information system for monitoring user opinion and its role for the public

et al. 2022

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“…This system allows working with texts in the Kazakh and Russian languages. It also has built-in modules for connecting to the application programming interfaces (APIs) of social networks: Vkontakte [40], Facebook [41,42], Twitter [43,44], Instagram [45,46], YouTube [47], Telegram [48], and Odnoklassniki [49]. The OMSystem automatically determines the language of the text (Russian, Kazakh, smiles/characters) and the sentiment of the topic, as negative, positive, or neutral, using a sentiment dictionary and ML algorithms.…”

Section: The Omsystem Information System Design Methodologymentioning

confidence: 99%

On the Development of an Information System for Monitoring User Opinion and its Role for the Public

Karyukin¹,

Mutanov²,

Mamykova³

et al. 2021

Preprint

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Social media services and analytics platforms are rapidly growing. A large number of various events happen mostly every day, and the role of social media monitoring tools is also increasing. Social networks are widely used for managing and promoting brands and different services. Thus, most popular social analytics platforms aim for business purposes while monitoring various social, economic, and political problems remains underrepresented and not covered by thorough research. Moreover, most of them focus on resource-rich languages such as the English language, whereas texts and comments in other low-resource languages such as the Russian and Kazakh languages in social media are not represented well enough. So, this work is devoted to developing and applying the information system called the OMSystem for analyzing users’ opinions on news portals, blogs, and social networks in Kazakhstan. The system uses sentiment dictionaries of the Russian and Kazakh languages and machine learning algorithms to determine the sentiment of social media texts. The whole structure and functionalities of the system are also presented. In the experimental part, the system’s monitoring of the healthcare, political and social aspects of the most relevant topics connected with the vaccination against the coronavirus disease are thoroughly observed and analyzed. The analysis allowed discovering the public social mood in the cities of Almaty and Nur-Sultan and other large regional cities of Kazakhstan. The system’s study included two extensive periods: 10-01-2021 to 30-05-2021 and 01-07-2021 to 12-08-2021. In the obtained results, people’s mood and attitude to the Government’s policies and actions were studied by such social network indicators as the level of topic discussion activity in society, the level of interest in the topic in society, and the mood level of society. These indicators calculated by the OMSystem allowed careful identification of alarming factors of the public (negative attitude to the government regulations, vaccination policies, trust to vaccination, etc.) and assessment of the social mood.

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“…Crowdsourcing Crowdsourcing refers to humancomputation systems where a large number of online users perform tasks that would typically be done by a designated agent or expert (Law and von Ahn 2011). Crowdsourcing has proven useful to cheaply label large SA datasets used in applications outside of education (Heidari and Shamsinejad 2020). In the educational context crowdsourcing has been utilized for the design and use of crowdsourced learning analytics tasks (Ahn et al 2021), as well as, to interpret learners' reviews of MOOCs (Li et al 2022), but neither to gather sentiment labels nor to serve as a hands-on learning activity for students themselves.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Assessing this feedback in a timely manner allows instructors to learn about course materials students struggle with, gain awareness of teams that have issues, or even identify students that fall behind (Ahadi et al 2015;Presler-Marshall, Heckman, and Stolee 2022;Gitinabard et al 2022;Neumann and Linzmayer 2021). Likert-type survey questions are easy to analyze but need to be carefully tailored to specific contexts and the responses tend to be unreliable (Holzbach 1978;Leising et al 2016;Murphy 1993). What is more, they offer limited detail as to what causes issues.…”

Section: Introductionmentioning

confidence: 99%

Practical Sentiment Analysis for Education: The Power of Student Crowdsourcing

Kasumba,

Neumman

2024

AAAI

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Sentiment analysis provides a promising tool to automatically assess the emotions voiced in written student feedback such as periodically collected unit-of-study reflections. The commonly used dictionary-based approaches are limited to major languages and fail to capture contextual differences. Pretrained large language models have been shown to be biased and online versions raise privacy concerns. Hence, we resort to traditional supervised machine learning (ML) approaches which are designed to overcome these issues by learning from domain-specific labeled data. However, these labels are hard to come by -- in our case manually annotating student feedback is prone to bias and time-consuming, especially in high-enrollment courses. In this work, we investigate the use of student crowdsourced labels for supervised sentiment analysis for education. Specifically, we compare crowdsourced and student self-reported labels with human expert annotations and use them in various ML approaches to evaluate the performance on predicting emotions of written student feedback collected from large computer science classes. We find that the random forest model trained with student-crowdsourced labels tremendously improves the identification of reflections with negative sentiment. In addition to our quantitative study, we describe our crowdsourcing experiment which was intentionally designed to be an educational activity in an introduction to data science course.

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Producing An Instagram Dataset For Persian Language Sentiment Analysis Using Crowdsourcing Method

Cited by 9 publications

References 5 publications

On the development of an information system for monitoring user opinion and its role for the public

On the development of an information system for monitoring user opinion and its role for the public

On the Development of an Information System for Monitoring User Opinion and its Role for the Public

Practical Sentiment Analysis for Education: The Power of Student Crowdsourcing

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