Analysis and Classification of Crime Tweets

Lal, Sangeeta; Tiwari, Lipika; Ranjan, Ravi; Verma, Ayushi; Sardana, Neetu; Mourya, Rahul

doi:10.1016/j.procs.2020.03.211

Cited by 37 publications

(17 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this supervising learning method, each of these tuples can be described by a set of attribute values. 31 The level of a restaurant will be predicted under those three algorithms, and their prediction accuracy will be evaluated.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Big Data Applications in Guangzhou Restaurants Analysis

Chang

2021

Big Data

View full text Add to dashboard Cite

With the development of modern information and communication technologies, such as the internet of things and big data analytics, businesses and users have become more adaptable to rapid changes. Both consumers and merchants have obtained great convenience. Meanwhile, a huge amount of data is generated. However, many businesses lack the ability to process these data, which contain critical business values. Therefore, this article uses data from the Dianping website to show how to use big data analytics techniques to exploit the valuable information from these raw data. First, descriptive analysis is conducted by using kernel density estimation. Then, multilinear regression analysis, Naive Bayes, and J48 are used to predict the level of restaurants. We found that flavor, environment, and service score are essential factors to the restaurant level. Moreover, J48 performs best among the three models with an accuracy of 88.89%.

show abstract

Section: Methodsmentioning

confidence: 99%

“…It is one of the supervising learning methods: given a data set, each of these tuples can be described by a set of attribute values. 31 Implement process can be explained as follows:…”

Section: Level Prediction Based On Machine Learningmentioning

confidence: 99%

Big Data Applications in Guangzhou Restaurants Analysis

Chang

2021

Big Data

View full text Add to dashboard Cite

show abstract

“…They trained the Naïve Bayesian, Random Forest, J48, and ZeroR models with the labeled data. The results showed that the best accuracy was attained by Random Forest with 98.1% [16].…”

Section: Review Of State Of the Artmentioning

confidence: 98%

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

Shoeibi

Hernández

et al. 2021

Electronics

View full text Add to dashboard Cite

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.

show abstract

“…They trained the Naïve Bayesian, Random Forest, J48, and ZeroR models with the labeled data. The results showed that the best accuracy was attained by Random Forest with 98.1% [15].…”

Section: Cauteruccio Et Al Inmentioning

confidence: 98%

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

Shoeibi¹,

Hernández²,

Chamoso³

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Analysis and Classification of Crime Tweets

Cited by 37 publications

References 8 publications

Big Data Applications in Guangzhou Restaurants Analysis

Big Data Applications in Guangzhou Restaurants Analysis

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

Contact Info

Product

Resources

About