Antisocial Behavior Monitoring Services of Indonesian Public Twitter Using Machine Learning

Astuti, Fitri Andri

doi:10.34123/icdsos.v2021i1.181

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…[6] - [8] analysed geolocated tweets and their correlation with crime rates and the presence of tourists and commuters in different locations, with insights into crime patterns and trends. [9], [10] have conducted research to help law enforcement agencies and policymakers better understand crime patterns and respond to emerging threats in real-time.…”

Section: Introductionmentioning

confidence: 99%

Expressivity of Tweets on Social Issues Using Aspect Based Text Classification

Thomas,

Parthasarathy

2023

IJRITCC

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Social discussions about crime on Twitter and open forums aims to understand the barriers that hinder people from expressing their concerns or aligning with popular opinions. A curated dataset spanning three months in 2023 was collected, covering categories like crimes and Gender Equality and Violence Against Women. The study employs aspect-based sentiment analysis to classify sentiment polarity in tweets, utilising a comprehensive framework involving three text feature classification stages. The initial stage analyses individual words, phrases, and tweet patterns to classify text features based on specific linguistic elements. In the subsequent step, semantic relations explore a better understanding of the core sentiment and infer relationships between different text keywords. This stage enhances the analysis by considering the meaning and contextual nuances of the language used in the tweets. The final stage incorporates transformer-based models for effective multilabel classification to view the diversity present in the dataset. The study's quantitative analysis reveals that the Ensemble learning approach demonstrates an impressive precision measure of 93%. By integrating the three stages of text feature classification, the study enhances the accuracy and comprehensiveness of sentiment analysis in social discussions about crime on Twitter.

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Section: Introductionmentioning

confidence: 99%

Expressivity of Tweets on Social Issues Using Aspect Based Text Classification

Thomas,

Parthasarathy

2023

IJRITCC

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A hybrid approach for the detection and monitoring of people having personality disorders on social networks

Ellouze

Belguith

2022

Soc. Netw. Anal. Min.

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Research in the medical field does not stop evolving. This evolution obliges doctors to be up-to-date in order to well manage every situation that may occur with their patients. However, the medical field is very sensitive and requires a great deal of precision, all of that poses a major problem. Consequently, there is a recourse to computer science, to resolve all of these issues. In this context, we propose in this paper an architecture, taking advantage of artificial intelligence (AI) and text mining techniques to: (i) identify individuals with personality disorder from their textual production on social networks by classifying their set of tweets into distinct classes representing respectively the presence, the category and the type of the disease and (ii) guarantee personalized monitoring by filtering inappropriate tweets according to patient’s circumstance. The first phase was achieved by taking advantage of a deep neuronal approach that benefits of: (i) CNN layers for features extraction from the textual part, (ii) two LSTM layers to preserve long-term dependencies between different lexical units, (iii) SVM classifier to detect the sick person using the dependency links found from the previous layer. The second phase was accomplished by applying a hybrid approach that combined linguistic and statistical techniques in order to filter inappropriate tweets according to the state of each patient. Following the evaluation of our approach, we acquire an F-measure rate equivalent to 84% for the detection of personality disorder, 64% for the detection of the type of disease and 70% for the task of filtering inappropriate content. The obtained results are motivating and may encourage researchers to improve them in view of the interest and the importance of this research axis.

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Interpreting the structure and results of a data warehouse model using ontology and machine learning techniques

Ellouze,

Belguith

2024

International Journal of Hybrid Intelligent Systems

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In this paper, we present an intelligent methodology for assisting decision-makers in both understanding the structure of a data warehouse model and making decisions. The support module proposed by our method comprises three operations: (i) transforming a data warehouse model into an ontology, allowing for the display of the different terminology related to a specific domain as well as the different semantic relationships between them, (ii) recommending a series of queries to the decision-maker that enables an understanding of the reasoning logic based on the ontology’s structure, (iii) enriching the different results obtained from some analysis tools through the use of advanced machine learning techniques. The originality of our proposed methodology lies in its ability to influence a decision-maker’s thinking in order to encourage him to take full advantage of the services provided by the data warehouse model. We apply our proposed methodology to an extended data warehouse model that enables the analysis of social media data related to people with personality disorders (PD). The primary goal of this model is to provide decision-makers with suitable services that allow them to make meaningful decisions for people with personality disorders around the world. This task was carried out by analyzing the activities and content of people on social media. In addition, one of the main advantages of this model is the use of various artificial intelligence (AI) and natural language processing (NLP) techniques. Our proposed methodology is implemented and the results achieved are evaluated using both quantitative and qualitative methods.

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Antisocial Behavior Monitoring Services of Indonesian Public Twitter Using Machine Learning

Cited by 3 publications

References 2 publications

Expressivity of Tweets on Social Issues Using Aspect Based Text Classification

Expressivity of Tweets on Social Issues Using Aspect Based Text Classification

A hybrid approach for the detection and monitoring of people having personality disorders on social networks

Interpreting the structure and results of a data warehouse model using ontology and machine learning techniques

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