Optimizing Short Message Text Sentiment Analysis for Mobile Device Forensics

Aboluwarin, Oluwapelumi; Andriotis, Panagiotis; Takasu, Atsuhiro; Tryfonas, Theo

doi:10.1007/978-3-319-46279-0_4

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…Preprocessing tasks involve applying natural language processing techniques to treat the texts by eliminating noise that affects the analytical process and formatting the text to perform subsequent processing. Examples of these preprocessing tasks include text cleaning and normalization, removing special characters, numbers, empty or white spaces, stop words, performing case folding, stemming, and lemmatizing, tokenization, and extraction of n-grams as evidenced by the work by Aboluwarin et al [62], Chandra et al [63], Gil et al [64], Martín et al [47], and Savaliya and Philip [65].…”

Section: Text Mining Techniques and Technologies Applied In Public Se...mentioning

confidence: 99%

Enabling Public Security Text-Based Analytics: A Survey to Outline Research Directions

De Carvalho,

Dos Santos,

Nepomuceno

et al. 2024

Preprint

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Text mining is a technological trend often highlighted in the continuous exchange of information through interconnected media. Its applicability goes beyond private organizations, as the public sector also requires it to treat textual information regarding services offered. Within this scenario, public security emerges as a prominent user of text mining that seeks to ensure the construction of data and knowledgebases to support decision-making about law enforcement actions to ensure citizen welfare. The primary objectives of this article are: (i) to develop a survey to identify text mining applications, techniques, opportunities, and challenges in public security, and (ii) to outline research directions concerning these topics and provide insights so that interested researchers can develop new studies. The literature was searched within four databases: Scopus, IEEE Xplore, ACM Digital Library, and Web of Science. A filtering process was applied to extract the works most aligned with the target theme, resulting in the selection of 194 of the most relevant works for a literature review. There were identified nineteen key applications of text mining related to public security and the most recurrent techniques and technologies reported between 2014 to 2021, supporting outlining three axes for future directions: one with possible expansion of objectives for new research; another on changes and adaptations in scopes for the methodological context; and the last one on expansions and changes in application scenarios based on the literature.

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Section: Text Mining Techniques and Technologies Applied In Public Se...mentioning

confidence: 99%

Enabling Public Security Text-Based Analytics: A Survey to Outline Research Directions

De Carvalho,

Dos Santos,

Nepomuceno

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Since it is desired to obtain the best model of topics for a given corpus, it is possible to define the best parameter setting (considering the number of topics and the learning decay) using Grid Search, which is a method for hyperparameter optimization involving the specification of a group of parameters, selecting the ones with the best cross-validation accuracy results (Aboluwarin et al, 2016;Li et al, 2014). Both LDA and Grid Search were applied using Scikit-Learn (Pedregosa et al, 2011), an extensive collection of machine learning methods for the Python language.…”

Section: Lht 424mentioning

confidence: 99%

Towards corpora creation from social web in Brazilian Portuguese to support public security analyses and decisions

Carvalho

Costa²

2022

LHT

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PurposeThis article presents two Brazilian Portuguese corpora collected from different media concerning public security issues in a specific location. The primary motivation is supporting analyses, so security authorities can make appropriate decisions about their actions.Design/methodology/approachThe corpora were obtained through web scraping from a newspaper's website and tweets from a Brazilian metropolitan region. Natural language processing was applied considering: text cleaning, lemmatization, summarization, part-of-speech and dependencies parsing, named entities recognition, and topic modeling.FindingsSeveral results were obtained based on the methodology used, highlighting some: an example of a summarization using an automated process; dependency parsing; the most common topics in each corpus; the forty named entities and the most common slogans were extracted, highlighting those linked to public security.Research limitations/implicationsSome critical tasks were identified for the research perspective, related to the applied methodology: the treatment of noise from obtaining news on their source websites, passing through textual elements quite present in social network posts such as abbreviations, emojis/emoticons, and even writing errors; the treatment of subjectivity, to eliminate noise from irony and sarcasm; the search for authentic news of issues within the target domain. All these tasks aim to improve the process to enable interested authorities to perform accurate analyses.Practical implicationsThe corpora dedicated to the public security domain enable several analyses, such as mining public opinion on security actions in a given location; understanding criminals' behaviors reported in the news or even on social networks and drawing their attitudes timeline; detecting movements that may cause damage to public property and people welfare through texts from social networks; extracting the history and repercussions of police actions, crossing news with records on social networks; among many other possibilities.Originality/valueThe work on behalf of the corpora reported in this text represents one of the first initiatives to create textual bases in Portuguese, dedicated to Brazil's specific public security domain.

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“…Their results show that maximum accuracy of 85.5% was achieved in the message level task. Aboluwarin et al (2016) performed the sentiment analysis on SMS messages and Twitter datasets for digital forensics. They considered three sentiments: positive, negative or neutral emotions and the nondictionary words were used as features along with other sentence level features.…”

Section: Related Workmentioning

confidence: 99%

Multilingual SMS-based author profiling: Data and methods

Fatima¹,

Anwar²,

Naveed³

et al. 2018

Nat. Lang. Eng.

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In the recent years, many benchmark author profiling corpora have been developed for various genres including Twitter, social media, blogs, hotel reviews and e-mail, etc. However, no such standard evaluation resource has been developed for Short Messaging Service (SMS), a popular medium of communication, which is very useful for author profiling. The primary aim of this study is to develop a large multilingual (English and Roman Urdu) benchmark SMS-based author profiling corpus. The proposed corpus contains 810 author profiles, wherein each profile consists of an aggregation of SMS messages as a single document of an author, along with seven demographic traits associated with each author profile: gender, age, native language, native city, qualification, occupation and personality type (introvert/extrovert). The secondary aims of this study include the following: (1) annotating the proposed corpus for code-switching annotations at the lexical level (approximately 0.69 million tokens are manually annotated for code-switching) and (2) applying the stylometry-based method (groups of sixty-four features) and the content-based method (twelve features) for gender identification in order to demonstrate how our proposed corpus can be used for the development and evaluation of various author profiling methods. The results show that the content-based character 5-gram feature outperformed all the other features by obtaining the accuracy score of 0.975 andF1score of 0.947 for gender identification while using the entire corpus. Furthermore, our proposed corpora (SMS–AP–18 and code-switched SMS–AP–18) are freely and publicly available for research purpose.

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Optimizing Short Message Text Sentiment Analysis for Mobile Device Forensics

Cited by 5 publications

References 21 publications

Enabling Public Security Text-Based Analytics: A Survey to Outline Research Directions

Enabling Public Security Text-Based Analytics: A Survey to Outline Research Directions

Towards corpora creation from social web in Brazilian Portuguese to support public security analyses and decisions

Multilingual SMS-based author profiling: Data and methods

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