Analysis of Popularity Sentiment in Opinion Presidential Election 2019 on Twitter

Sunge, Aswan Supriyadi

doi:10.4108/eai.17-7-2020.2302958

Cited by 3 publications

(3 citation statements)

References 5 publications

(6 reference statements)

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“…This section describes sentiment analysis, which entails examining a text to discern its emotional content. There are numerous approaches to sentiment analysis [23], consisting of three primary approaches: methods based on rules, features, and embeddings [24][25][26][27]. The process of analyzing sentiments in tweets involves the utilization of rule-based models to assess the subjectivity of an input text, distinguishing between factual statements and opinions.…”

Section: Analysis Of Sentiments In Tweetsmentioning

confidence: 99%

Analysis of social networks and filtering of Arabic crime tweets based on an intelligent dictionary using a genetic algorithm

Zainab Khyioon Abdalrdha,

Abbas Mohsin Al-Bakry,

Alaa K. Farhan

2024

Global J. Eng. Technol. Adv.

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Preserving a robust online community poses a significant difficulty due to the unrestricted flexibility members have in expressing themselves and behaving. This issue can be remedied through the implementation of user behavior monitoring and analysis, followed by the implementation of suitable actions. The objective of this research is to develop an intelligent dictionary using the genetic algorithm to identify and categorize Twitter posts related to criminal activities by collecting data from tweets. Once the data is preserved, graph analysis techniques are employed to evaluate interactions between users. Next, the user behavior is analyzed using metadata analysis, whereby the chronology associated with each user profile is obtained. Furthermore, the study analyzes the behavioral patterns of users over time. Afterward, a method based on rules is used to create a structure for aspect-based analysis of sentiment. This method assesses the subjectivity of the input text, distinguishing between factual information and personal opinions. Additionally, transformer-based sentiment analysis determines whether the tweet evokes positive or negative sentiment. Furthermore, the task involves constructing a model that can accurately categorize a tweet based on its relevance to criminal activity. Ultimately, the intelligent dictionary is utilized to identify and isolate anomalous behavior by selecting provocative profiles. A Twitter profile exhibiting significant similarity with criminal cases presents a perilous risk to society.

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Section: Analysis Of Sentiments In Tweetsmentioning

confidence: 99%

Analysis of social networks and filtering of Arabic crime tweets based on an intelligent dictionary using a genetic algorithm

Zainab Khyioon Abdalrdha,

Abbas Mohsin Al-Bakry,

Alaa K. Farhan

2024

Global J. Eng. Technol. Adv.

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“…Sentiment analysis is the process of interpreting a text and discovering its emotions. There are many different ways to perform sentiment analysis [40], including three main solutions: rule-based [41], feature-based [42], and embedding-based methods [43,44]. The aspect-based sentiment analysis model is implemented using rule-based models to measure an input text's subjectivity, indicating if a tweet is a fact or an opinion, and transformerbased sentiment analysis to understand if a tweet induces positive or negative emotions.…”

Section: Aspect-based Sentiment Analysismentioning

confidence: 99%

AI-Crime Hunter: An AI Mixture of Experts for Crime Discovery on Twitter

Shoeibi

Hernández

et al. 2021

Electronics

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Maintaining a healthy cyber society is a great challenge due to the users’ freedom of expression and behavior. This can be solved by monitoring and analyzing the users’ behavior and taking proper actions. This research aims to present a platform that monitors the public content on Twitter by extracting tweet data. After maintaining the data, the users’ interactions are analyzed using graph analysis methods. Then, the users’ behavioral patterns are analyzed by applying metadata analysis, in which the timeline of each profile is obtained; also, the time-series behavioral features of users are investigated. Then, in the abnormal behavior detection and filtering component, the interesting profiles are selected for further examinations. Finally, in the contextual analysis component, the contents are analyzed using natural language processing techniques; a binary text classification model (SVM (Support Vector Machine) + TF-IDF (Term Frequency—Inverse Document Frequency) with 88.89% accuracy) is used to detect if a tweet is related to crime or not. Then, a sentiment analysis method is applied to the crime-related tweets to perform aspect-based sentiment analysis (DistilBERT + FFNN (Feed-Forward Neural Network) with 80% accuracy), because sharing positive opinions about a crime-related topic can threaten society. This platform aims to provide the end-user (the police) with suggestions to control hate speech or terrorist propaganda.

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“…Sentiment Analysis is the process of interpreting a text and discovering its emotions. There are many different ways to do sentiment analysis [39]. There are three types of solutions to perform Sentiment Analysis; Rule-Based [40], Feature-Based [41], and Embedding-Based methods [42,43].…”

Section: Aspect-based Sentiment Analysismentioning

confidence: 99%

AI-Crime Hunter: An AI Mixture of Experts for Crime Discovery on Twitter

Shoeibi¹,

Hernández²,

Chamoso³

et al. 2021

Preprint

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Maintaining a healthy cyber society is a big challenge due to the users’ freedom of expression and behaving. It can be solved by monitoring and analyzing the users’ behavior and taking proper actions towards them. This research aims to present a platform that monitors the public content on Twitter by extracting tweet data. After maintaining the data, the users’ interactions are analyzed using Graph Analysis methods. Then the users’ behavioral patterns are analyzed by applying Metadata Analysis, in which the timeline of each profile is obtained; also, the time-series behavioral features of users are investigated. Then in the Abnormal Behavior Detection Filtering component, the interesting profiles are selected for further examinations. Finally, in the Contextual Analysis component, the contents will be analyzed using natural language processing techniques; A binary text classification model (SVM + TF-IDF with 88.89% accuracy) for detecting if the tweet is related to crime or not. Then, a sentiment analysis method is applied to the crime-related tweets to perform aspect-based sentiment analysis (DistilBERT + FFNN with 80% accuracy); because sharing positive opinions about a crime-related topic can threaten society. This platform aims to provide the end-user (Police) suggestions to control hate speech or terrorist propaganda.

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Analysis of Popularity Sentiment in Opinion Presidential Election 2019 on Twitter

Cited by 3 publications

References 5 publications

Analysis of social networks and filtering of Arabic crime tweets based on an intelligent dictionary using a genetic algorithm

Analysis of social networks and filtering of Arabic crime tweets based on an intelligent dictionary using a genetic algorithm

AI-Crime Hunter: An AI Mixture of Experts for Crime Discovery on Twitter

AI-Crime Hunter: An AI Mixture of Experts for Crime Discovery on Twitter

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