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Saraiva M, Matijošaitienė I, Mishra S, Amante A. Crime Prediction and Monitoring in Porto, Portugal, Using Machine Learning, Spatial and Text Analytics. IJGI 2022; 11:400. [DOI: 10.3390/ijgi11070400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Crimes are a common societal concern impacting quality of life and economic growth. Despite the global decrease in crime statistics, specific types of crime and feelings of insecurity, have often increased, leading safety and security agencies with the need to apply novel approaches and advanced systems to better predict and prevent occurrences. The use of geospatial technologies, combined with data mining and machine learning techniques allows for significant advances in the criminology of place. In this study, official police data from Porto, in Portugal, between 2016 and 2018, was georeferenced and treated using spatial analysis methods, which allowed the identification of spatial patterns and relevant hotspots. Then, machine learning processes were applied for space-time pattern mining. Using lasso regression analysis, significance for crime variables were found, with random forest and decision tree supporting the important variable selection. Lastly, tweets related to insecurity were collected and topic modeling and sentiment analysis was performed. Together, these methods assist interpretation of patterns, prediction and ultimately, performance of both police and planning professionals.
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Islam SS, Haque MS, Miah MSU, Sarwar TB, Bhowmik A. A Trend Analysis of crimes in Bangladesh. Proceedings of the 2nd International Conference on Computing Advancements 2022. [DOI: 10.1145/3542954.3543026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Affiliation(s)
| | - Md. Samiul Haque
- Institute of Information Technology, University of Dhaka, Bangladesh
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Zhang Y, Shirakawa M, Wang Y, Li Z, Hara T. Twitter-aided decision making: a review of recent developments. APPL INTELL 2022; 52:13839-13854. [PMID: 35250174 PMCID: PMC8881980 DOI: 10.1007/s10489-022-03241-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/13/2022] [Indexed: 11/27/2022]
Abstract
AbstractTwitter is one of the largest online platforms where people exchange information. In the first few years since its emergence, researchers have been exploring ways to use Twitter data in various decision making scenarios, and have shown promising results. In this review, we examine 28 newer papers published in last five years (since 2016) that continued to advance Twitter-aided decision making. The application scenarios we cover include product sales prediction, stock selection, crime prevention, epidemic tracking, and traffic monitoring. We first discuss the findings presented in these papers, that is how much decision making performance has been improved with the help of Twitter data. Then we offer a methodological analysis that considers four aspects of methods used in these papers, including problem formulation, solution, Twitter feature, and information transformation. This methodological analysis aims to enable researchers and decision makers to see the applicability of Twitter-aided methods in different application domains or platforms.
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Affiliation(s)
- Yihong Zhang
- Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
| | - Masumi Shirakawa
- Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
| | - Yuanyuan Wang
- Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Ube, Japan
| | - Zhi Li
- Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
| | - Takahiro Hara
- Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
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Shoeibi N, Shoeibi N, Hernández G, Chamoso P, Corchado JM. AI-Crime Hunter: An AI Mixture of Experts for Crime Discovery on Twitter. Electronics 2021; 10:3081. [DOI: 10.3390/electronics10243081] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
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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Abstract
Words are one of the most essential elements of expressing sentiments in context although they are not the only ones. Also, syntactic relationships between words, morphology, punctuation, and linguistic phenomena are influential. Merely considering the concept of words as isolated phenomena causes a lot of mistakes in sentiment analysis systems. So far, a large amount of research has been conducted on generating sentiment dictionaries containing only sentiment words. A number of these dictionaries have addressed the role of combinations of sentiment words, negators, and intensifiers, while almost none of them considered the heterogeneous effect of the occurrence of multiple linguistic phenomena in sentiment compounds. Regarding the weaknesses of the existing sentiment dictionaries, in addressing the heterogeneous effect of the occurrence of multiple intensifiers, this research presents a sentiment dictionary based on the analysis of sentiment compounds including sentiment words, negators, and intensifiers by considering the multiple intensifiers relative to the sentiment word and assigning a location-based coefficient to the intensifier, which increases the covered sentiment phrase in the dictionary, and enhanced efficiency of proposed dictionary-based sentiment analysis methods up to 7% compared to the latest methods.
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Affiliation(s)
- Hamed Zargari
- Department of Computer and IT Engineering, Shahrood University of Technology Shahrood, Iran
| | - Morteza Zahedi
- Department of Computer and IT Engineering, Shahrood University of Technology Shahrood, Iran
| | - Marziea Rahimi
- Department of Computer and IT Engineering, Shahrood University of Technology Shahrood, Iran
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Priyadarshini I, Mohanty P, Kumar R, Sharma R, Puri V, Singh PK. A study on the sentiments and psychology of twitter users during COVID-19 lockdown period. Multimed Tools Appl 2021; 81:27009-27031. [PMID: 34149302 PMCID: PMC8200552 DOI: 10.1007/s11042-021-11004-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 03/17/2021] [Accepted: 05/04/2021] [Indexed: 06/12/2023]
Abstract
The outbreak of the novel Coronavirus in late 2019 brought severe devastation to the world. The pandemic spread across the globe, infecting more than ten million people and disrupting several businesses. Although social distancing and the use of protective masks were suggested all over the world, the cases seem to rise, which led to worldwide lockdown in different phases. The rampant escalation in the number of cases, the global effects, and the lockdown may have a severe effect on the psychology of people. The emergency protocols implemented by the authorities also lead to increased use in the number of multimedia devices. Excessive use of such devices may also contribute to psychological disorders. Hence, hence it is necessary to analyze the state of mind of people during the lockdown. In this paper, we perform a sentiment analysis of Twitter data during the pandemic lockdown, i.e., two weeks and four weeks after the lockdown was imposed. Investigating the sentiments of people in the form of positive, negative, and neutral tweets would assist us in determining how people are dealing with the pandemic and its effects on a psychological level. Our study shows that the lockdown witnessed more number positive tweets globally on multiple datasets. This is indicative of the positivity and optimism based on the sentiments and psychology of Twitter users worldwide. The study will be effective in determining people's mental well-being and will also be useful in devising appropriate lockdown strategies and crisis management in the future.
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Affiliation(s)
- Ishaani Priyadarshini
- Department of Electrical and Computer Science, University of Delaware, Newark, DE 19716 USA
| | - Pinaki Mohanty
- Department of Computer Science, Purdue University, 610 Purdue Mall, West Lafayette, IN 47907 USA
| | - Raghvendra Kumar
- Department of Computer Science and Engineering, GIET University, Gunupur, India
| | - Rohit Sharma
- Department of Electronics & Communication Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, NCR Campus, Delhi- NCR Campus, Delhi-Meerut Road, Modinagar, Ghaziabad, UP India
| | - Vikram Puri
- Center of Visualization and Simulation, Duy Tan University, Da Nang, Vietnam
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Priyadarshini I, Cotton C. A novel LSTM-CNN-grid search-based deep neural network for sentiment analysis. J Supercomput 2021; 77:13911-13932. [PMID: 33967391 PMCID: PMC8097246 DOI: 10.1007/s11227-021-03838-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/21/2021] [Indexed: 06/01/2023]
Abstract
As the number of users getting acquainted with the Internet is escalating rapidly, there is more user-generated content on the web. Comprehending hidden opinions, sentiments, and emotions in emails, tweets, reviews, and comments is a challenge and equally crucial for social media monitoring, brand monitoring, customer services, and market research. Sentiment analysis determines the emotional tone behind a series of words may essentially be used to understand the attitude, opinions, and emotions of users. We propose a novel long short-term memory (LSTM)-convolutional neural networks (CNN)-grid search-based deep neural network model for sentiment analysis. The study considers baseline algorithms like convolutional neural networks, K-nearest neighbor, LSTM, neural networks, LSTM-CNN, and CNN-LSTM which have been evaluated using accuracy, precision, sensitivity, specificity, and F-1 score, on multiple datasets. Our results show that the proposed model based on hyperparameter optimization outperforms other baseline models with an overall accuracy greater than 96%.
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Affiliation(s)
- Ishaani Priyadarshini
- Department of Electrical and Computer Engineering, University of Delaware, Newark, USA
| | - Chase Cotton
- Department of Electrical and Computer Engineering, University of Delaware, Newark, USA
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Sathish Kumar PJ, Nancy VAO, Sathish N, Kajendran K, Pugazhendi N, Balaji S. High-Performance Disease Prediction and Recommendation Generation Healthcare System Using I3 Algorithm. Micro-Electronics and Telecommunication Engineering 2021:41-52. [DOI: 10.1007/978-981-33-4687-1_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
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