Pre processing of social media remarks for forensics

The Internet's rapid growth has led to a surge in social network users, resulting in an increase in extreme emotional and hate speech online. This study focuses on the security of public opinion in cyber security by analyzing Twitter data. The goal is to develop a model that can detect both sentiment and hate speech in user texts, aiding in the identification of content that may violate laws and regulations. The study involves pre processing the acquired forensic data, including tasks like lowercasing, stop word removal, and stemming, to obtain clear and effective data. This paper contributes to the field of public opinion security by linking forensic data with machine learning techniques, showcasing the potential for detecting and analyzing Twitter text data

Enhancing Hate Speech Detection in the Digital Age : A Novel Model Fusion Approach Leveraging a Comprehensive Dataset

The authors extend their appreciation to the Arab Open Uni-versity for funding this work through AOU research fund No.(AOUKSA-524008)Peer reviewe

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

Synonym based feature expansion for Indonesian hate speech detection

Online hate speech is one of the negative impacts of internet-based social media development. Hate speech occurs due to a lack of public understanding of criticism and hate speech. The Indonesian government has regulations regarding hate speech, and most of the existing research about hate speech only focuses on feature extraction and classification methods. Therefore, this paper proposes methods to identify hate speech before a crime occurs. This paper presents an approach to detect hate speech by expanding synonyms in word embedding and shows the classification comparison result between Word2Vec and FastText with bidirectional long short-term memory which are processed using synonym expanding process and without it. The goal is to classify hate speech and non-hate speech. The best accuracy result without the synonym expanding process is 0.90, and the expanding synonym process is 0.93

Detection of Hateful Comments on Social Media

Social media usage has grown tremendously in the contemporary communication landscape. Along with its numerous benefits, some users abuse the channels by spreading hatred, far from the intended purpose of building connections on a personal level. To date, an empirical method for detecting, quantifying, and categorizing hateful comments on social networks comprehensively and proactively is still lacking. Besides, majority of the cases remain unreported due to social confounders such as fear of victimization and the psychological implications of hateful comments, leading to a situation whereby, the detrimental effect of the situation is underestimated. The ill-defined situation in the growing online space impedes progress towards developing mechanisms and policies to mitigate the harmful effects of hate on social media, ultimately reducing the effectiveness of the platforms as effective communication tools. This proposal suggests Naïve Bayes classifier as a novel approach for detecting and classifying hateful social media comments to bridge this gap. Data set was taken from set provided by Kaggle and consisted of 30,000 Tweets. From the results of the use of this method, it was calculated that Bayes method is 62.75% accurate, which is not satisfactory. However, to bridge accuracy gap, nural algorithm was used which gain an improved accuracy of 87%

A review on deep-learning-based cyberbullying detection

Bullying is described as an undesirable behavior by others that harms an individual physically, mentally, or socially. Cyberbullying is a virtual form (e.g., textual or image) of bullying or harassment, also known as online bullying. Cyberbullying detection is a pressing need in today’s world, as the prevalence of cyberbullying is continually growing, resulting in mental health issues. Conventional machine learning models were previously used to identify cyberbullying. However, current research demonstrates that deep learning surpasses traditional machine learning algorithms in identifying cyberbullying for several reasons, including handling extensive data, efficiently classifying text and images, extracting features automatically through hidden layers, and many others. This paper reviews the existing surveys and identifies the gaps in those studies. We also present a deep-learning-based defense ecosystem for cyberbullying detection, including data representation techniques and different deep-learning-based models and frameworks. We have critically analyzed the existing DL-based cyberbullying detection techniques and identified their significant contributions and the future research directions they have presented. We have also summarized the datasets being used, including the DL architecture being used and the tasks that are accomplished for each dataset. Finally, several challenges faced by the existing researchers and the open issues to be addressed in the future have been presented

A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and Applications

Sentiment analysis (SA) is an emerging field in text mining. It is the process of computationally identifying and categorizing opinions expressed in a piece of text over different social media platforms. Social media plays an essential role in knowing the customer mindset towards a product, services, and the latest market trends. Most organizations depend on the customer's response and feedback to upgrade their offered products and services. SA or opinion mining seems to be a promising research area for various domains. It plays a vital role in analyzing big data generated daily in structured and unstructured formats over the internet. This survey paper defines sentiment and its recent research and development in different domains, including voice, images, videos, and text. The challenges and opportunities of sentiment analysis are also discussed in the paper. \keywords{Sentiment Analysis, Machine Learning, Lexicon-based approach, Deep Learning, Natural Language Processing