A NLP framework based on meaningful latent-topic detection and sentiment analysis via fuzzy lattice reasoning on youtube comments

Jelodar, Hamed; Wang, Yongli; Rabbani, Mahdi; Ahmadi, Sajjad Bagheri Baba; Boukela, Lynda; Zhao, Ruxin; Larik, Raja Sohail Ahmed

doi:10.1007/s11042-020-09755-z

Cited by 29 publications

(11 citation statements)

References 49 publications

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“…Larik. [19] proposed a Natural language programming (NLP) based sentiment analysis method for YouTube. The proposed method uses a fuzzy logic algorithm to find the exact meaning of the users' comments.…”

Section: Related Workmentioning

confidence: 99%

The Security System in Information Technology Detects Fake Accounts on The Youtube Platform

2023

IJIES

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YouTube platform provides paid/ unpaid video services for different users for promotions, education, learning, information sharing, etc. Accessing this platform's content/ channel-based advancements is based on user identification through registration/ credential sharing. In recent years, accessing information through fake accounts has increased other users' illegitimacy and security demands. The article introduces a temporal behavioral method (TBM) using the classified learning (CL) technique to address the impact of fake users on YouTube-like platforms. The proposed method eyes on user credentials, interactions, and access information under different usage series. The data used for the classification learning identifies the active, inactive, and false searches/ information accessed by the user. With the recent meetings removed, the static and inaccurate searches are segregated using the learning process from the last known access session. Rather than applying new rules to the identified account/user, false user flagging reported data sharing and illegitimacy is all avoided. Based on the different session logins and the recommendations by the learning process, the fraudulent users are restricted from communicating with other users and legitimate information. Maximizing data sharing and user detection is facilitated by the better recognizability afforded by the prolonged inactive session categorization following in succession. The suggested TBM-CL strategy addresses the impact of fake users on YouTube-like platforms and increases the maximums for user detection (14.93%), information sharing (11.3%), and classifications (15.93%) across all sessions. It decreases the erroneous ratio by 8.03% and the delay by 9.07% based on the dataset.

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Section: Related Workmentioning

confidence: 99%

The Security System in Information Technology Detects Fake Accounts on The Youtube Platform

2023

IJIES

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“…Developed in Python, Textblob is a tool for manipulating large amounts of text. For the purpose of conducting NLP operations, it provides a standard application programming interface (API) [9]. It is identical to a string written in Python [17].…”

Section: Textblobmentioning

confidence: 99%

“…It was helpful to conceptualize the entire situation by dividing the polarity of the received feelings into three groups: positive, negative, and neutral. The findings showed that 33.96% of the replies were favourable, 17.55% were negative, and 48.49% were neutral [9]. They also included a timeline analysis of tweets in this poll because respondents' emotions changed over time.…”

Section: Introductionmentioning

confidence: 99%

Predicting COVID-19 vaccinators based on machine learning and sentiment analysis

2023

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In the past two years, the world witnessed the spread of the coronavirus (COVID-19) pandemic that disrupted the entire world, the only solution to this epidemic was health isolation, and with it everything stopped. When announcing the availability of a vaccine, the world was divided over the effectiveness and harms of this vaccine. This article provides an analysis of vaccinators and analysis of people's opinions of the vaccine's efficacy and whether negative or positive. Then a model is built to predict the future numbers of vaccinators and a model that predicts the number of negative opinions or tweets. The model consists of three stages: first, converting data sets into a synchronized time series, that is, the same place and time for vaccination and tweets. The second stage is building a prediction model and the third stage was descripting analysis of the prediction results. The autoregressive integrated moving averages (ARIMA) method was used after decomposing the components of ARIMA and choosing the optimal model, the best results obtained from seasonal ARIMA (SARIMA) for both predictions, the last stage is the descriptive analysis of the results and linking them together to obtain an analysis describing the change in the number of vaccinators and the number of negative tweets.

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“…A considerable body of research has utilized the users' comments on YouTube-based videos to generate knowledge about viewers' responses, opinions, attitudes, and counter-views regarding the posted videos. (Jelodar, et. al.…”

Section: Youtube Comments Analysismentioning

confidence: 99%

Mediatizing Egyptian Families: Attitudes towards YouTube Household Vlogs

Abdou

2020

المجلة العربیة لبحوث الاعلام والاتصال

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Mediatizing the old personal home videos and customizing the modern reality-based television shows, YouTube home vlogging emerged as a successful embodiment of the fading-away frontiers between the private and the public in the new media era. Exploiting the affordability, accessibility, and portability of the video production equipment along with the participatory and interactive nature of the Web 2, several Egyptian spouses have initiated their own household video channels, revealing various aspects of what was previously known as their private life. This study explores the Egyptian users' (viewers') perceptions of this new "homecasting" practice and their attitudes towards the content and its creators through a cultural reception analysis lens. Qualitative Thematic Analysis was employed to detect the salient themes and general valence of the users› comments on a sample of videos from two of the users' comments "Ahemd Hassan & Zeinab" and "Diaries of Hamdy & Wafaa." Most of the viewers showed favorable attitudes towards the content, and tones of comments were mostly supportive and defensive of the creators, indicating an accepting approach to the homebased vlogging phenomenon. Comments featured both negative and positive themes but lacked the potential for creating a productive social debate. 2020-‫ديسمرب‬ / ‫أكتوبر‬) 31 (‫العدد‬-‫واالتصال‬ ‫االعالم‬ ‫لبحوث‬ ‫العربية‬ ‫املجلة‬ Reception AnalysisTheory The traditional content analysis focuses mainly on how the meaning is explicitly presented to the audience, assuming that the meaning, delivered within the content, is a "common ground" between the producer and the receiver of the content itself. (Kepplinger, 1989) In a similar context, (Livingstone, 1989) argued that content analysis gives the researchers the authority to identify, code, interpret the manifest meanings of media content regardless of how the audience members understand and process those meanings. That is why she suggested a subsequent audience reception investigation to complement the findings of the content study. This view coincides with the phenomenological perspective that adopts the medium-audience formula in the process of mean-making, where the question is not "what" something like a photograph or painting means, but "how" it means.(Jensen, 1993) That notion was the springboard for the "reception studies" or "audience reception analysis" which saw its evolution and development as a theoretical and analytical approach during the 1980s. (Livingstone, 1998) Reception theorists emphasize the interaction that occurs between the media texts (all forms of mediated content) and the audience members, currently the media users. They consider this type of media-user engagement as the area of 'meaning reproduction'. Accordingly, the meanings are neither fixed nor isolated; instead, they are developed throughout the 'reading/viewing' experience and the users' 'response' to the media text. (Barry, 2004) Reception analysis approach focuses on the media audience's response to and interaction with the media ...

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A NLP framework based on meaningful latent-topic detection and sentiment analysis via fuzzy lattice reasoning on youtube comments

Cited by 29 publications

References 49 publications

The Security System in Information Technology Detects Fake Accounts on The Youtube Platform

The Security System in Information Technology Detects Fake Accounts on The Youtube Platform

Predicting COVID-19 vaccinators based on machine learning and sentiment analysis

Mediatizing Egyptian Families: Attitudes towards YouTube Household Vlogs

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