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Combining textual features to detect cyberbullying in social media posts

Meisy Fortunatus, Patricia Anthony, Stuart Charters
2020 Procedia Computer Science  
Cyberbullying has become prevalent in social media communication. To create a safe space for cyber communication, an effective cyberbullying detection method is needed.  ...  Abstract Cyberbullying has become prevalent in social media communication. To create a safe space for cyber communication, an effective cyberbullying detection method is needed.  ...  , a social media to ask 1-on-1 questions.  ... 
doi:10.1016/j.procs.2020.08.063 fatcat:vrupwv3bhndkbntv57iiwb2o4y

Affect corpus 2.0

Ricardo A. Calix, Gerald M. Knapp
2011 Proceedings of the second annual ACM conference on Multimedia systems - MMSys '11  
Objectives  Interest in emotion detection and emotion prediction: Advertisements  Human-computer interactions  Social media mining  Resources are needed to train and test emotion prediction models  ...  boundaries  Results of inter-annotator agreement analysis Annotation Tool Text corpus Statistics  176 stories by 3 authors.  15,302 sentences  Neutral class: all emotion class magnitudes are zero  ...  Applications  Speech/Text-to-Scene processing  Text-to-Speech processing  HCI  Calibration of emotion recognition within multimedia systems  Social media content analysis and Twitter dialog censoring  ... 
doi:10.1145/1943552.1943570 dblp:conf/mmsys/CalixK11 fatcat:yhuhbg4wh5dg3ktzujznvfbpv4


Neetika .
2017 International Journal of Advanced Research in Computer Science  
Nowadays social media has become the strongest platform for communication.  ...  The main focus of paper is on understanding of code mixed social media text in English and Punjabi.  ...  Meta tags, URL tags, Hash tags Some people on social media creatively use emoticons, Meta tags, URL tags, Hash tags in Social Media Text.  ... 
doi:10.26483/ijarcs.v8i8.4847 fatcat:g5xfieqhwvfbtoqpibmccqajga

Integrating Boundary Assembling into a DNN Framework for Named Entity Recognition in Chinese Social Media Text [article]

Zhaoheng Gong, Ping Chen, Jiang Zhou
2020 arXiv   pre-print
Named entity recognition is a challenging task in Natural Language Processing, especially for informal and noisy social media text.  ...  Chinese word boundaries are also entity boundaries, therefore, named entity recognition for Chinese text can benefit from word boundary detection, outputted by Chinese word segmentation.  ...  Conclusion In this paper we integrate a boundary assembling step with an LSTM module and a CRF module for Named Entity Recognition in Chinese social media text.  ... 
arXiv:2002.11910v1 fatcat:agpbv2wbojfxtm2djhxpswrii4

Analysis of Twitter Specific Preprocessing Technique for Tweets

Dharini Ramachandran, R Parvathi
2019 Procedia Computer Science  
Social media plays an important role in capturing the thoughts of people in their own representation of sentences.  ...  Abstract Social media plays an important role in capturing the thoughts of people in their own representation of sentences.  ...  Some of the popular applications of text analytics on social media are News Detection, Event detection, Sentiment Dharini Ramachandran,Parvathi R/ Procedia Computer Science 00 (2019) 000-000 Figure 1  ... 
doi:10.1016/j.procs.2020.01.083 fatcat:tx3kjq2xz5hndmokxcmcohzr64

Automatic Text Formatting for Social Media Based on Linefeed and Comma Insertion [chapter]

Masaki Murata, Tomohiro Ohno, Shigeki Matsubara
2011 Smart Innovation, Systems and Technologies  
This paper proposes a method for automatically formatting Japanese texts in social media. Our method formats texts by inserting commas and linefeeds appropriately.  ...  On transmitted texts in social media, commas and linefeeds are inserted incorrectly, and it becomes a factor of low-quality texts.  ...  This paper proposes a method for automatically formatting Japanese texts in social media. Our method formats texts by inserting commas and linefeeds at proper positions.  ... 
doi:10.1007/978-3-642-22158-3_28 fatcat:qeroc6qoyjdyhiislzoy6x2g5m


2021 Zenodo  
Social media platforms hold a vast volume of raw data that has been posted by people in the forms of texts, images, audio and video. People use this medium to express their thoughts and opinions.  ...  The identification of mental health can be detected using several data domains such as: sensors, text, structured data, and multi-modal system use.  ...  For depression analysis Sentence-level analysis is being used, as it is directly collected from the users [2] . People share various post in social media which contains only a few sentences.  ... 
doi:10.5281/zenodo.5392869 fatcat:226k3nzxcrf6rkzvkkw2fwobya

Multi-modal summarization of key events and top players in sports tournament videos

Dian Tjondronegoro, Xiaohui Tao, Johannes Sasongko, Cher Han Lau
2011 2011 IEEE Workshop on Applications of Computer Vision (WACV)  
However, web and social media articles with no time-stamps have not been fully leveraged, despite they are increasingly used to complement the coverage of major sporting tournaments.  ...  To detect and annotate the key events of live sports videos, we need to tackle the semantic gaps of audio-visual information.  ...  However, this text analysis ladder only applies for the text extracted from videos, not from Web and social media.  ... 
doi:10.1109/wacv.2011.5711541 dblp:conf/wacv/TjondronegoroTSL11 fatcat:sy4wundtnvejzakhxrdfoyhqsq

Indonesian Sentence Boundary Detection using Deep Learning Approaches

Joan Santoso, Esther Irawati Setiawan, Christian Nathaniel Purwanto, Fachrul Kurniawan
2021 Knowledge Engineering and Data Science  
Detecting the sentence boundary is one of the crucial pre-processing steps in natural language processing.  ...  We have proved that our approach works for Indonesian text using pre-trained embedding in Indonesian, as in previous studies. This study achieved an F1-Score value of 98.49 percent.  ...  Acknowledgment The authors want to appreciate Institut Sains dan Teknologi Terpadu Surabaya (ISTTS) for supporting this research.  ... 
doi:10.17977/um018v4i12021p38-48 fatcat:4kpccnvkjvap5lyaphof2qgiju

Feature Fusion for Negation Scope Detection in Sentiment Analysis: Comprehensive Analysis over Social Media

Nikhil Kumar Singh, Deepak Singh
2019 International Journal of Advanced Computer Science and Applications  
Explore text feature POS, BOW and HT with negation cue and scope detection techniques for classification technique over social media data set.  ...  This paper present a framework for feature fusion of text feature extraction, negation cue and scope detection technique for enhancing the performance of recent sentiment classifier for negation control  ...  detection over social media data set.  ... 
doi:10.14569/ijacsa.2019.0100580 fatcat:mgynkxvazfexhjj4qjduyzhczm

Evaluating Deep Learning Approaches for Covid19 Fake News Detection [article]

Apurva Wani, Isha Joshi, Snehal Khandve, Vedangi Wagh, Raviraj Joshi
2021 arXiv   pre-print
Social media platforms like Facebook, Twitter, and Instagram have enabled connection and communication on a large scale.  ...  We look at automated techniques for fake news detection from a data mining perspective.  ...  We would also like to thank the competition organizers for providing us an opportunity to explore the domain.  ... 
arXiv:2101.04012v2 fatcat:2ny3dzconfhxhgqzbm3mtm53y4

SoMaJo: State-of-the-art tokenization for German web and social media texts

Thomas Proisl, Peter Uhrig
2016 Proceedings of the 10th Web as Corpus Workshop  
In this paper we describe SoMaJo, a rulebased tokenizer for German web and social media texts that was the best-performing system in the EmpiriST 2015 shared task with an average F 1 -score of 99.57.  ...  media texts: (1) Tokenization and (2) part-of-speech tagging.  ...  The EmpiriST 2015 shared task on automatic linguistic annotation of computer-mediated communication / social media (Beißwenger et al., 2016) consists of two subtasks that deal with NLP for web and social  ... 
doi:10.18653/v1/w16-2607 dblp:conf/aclwac/ProislU16 fatcat:r4kcuutfjncnnllxw36mzl4nia

A Comprehensive Survey on Sentiment Analysis Using Machine Learning Techniques

Naeem Ahmed, Tariq Shah, Wakeel Ahmad, S.M. Adnan Shah
2020 University of Sindh Journal of Information and Communication Technology  
We can use data from social media platforms, blogs, product reviews websites and E-Commerce websites for using this data for analysis purposes.  ...  Various machine learning techniques can be applied on this data for getting useful insights that helps in decision making.  ...  PROCESS OF SENTIMENT ANALYSIS There are a few steps that are used to analyze any text for sentiment detection.  ... 
doaj:31c4573d086545468e4fc12715a5d8d4 fatcat:z7dauqjggbfhtgrhuga6g4lqki

Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions [article]

Izzat Alsmadi, Kashif Ahmad, Mahmoud Nazzal, Firoj Alam, Ala Al-Fuqaha, Abdallah Khreishah, Abdulelah Algosaibi
2021 arXiv   pre-print
These vulnerabilities allow adversaries to launch a diversified set of adversarial attacks on these algorithms in different applications of social media text processing.  ...  In this paper, we provide a comprehensive review of the main approaches for adversarial attacks and defenses in the context of social media applications with a particular focus on key challenges and future  ...  Acknowledgment The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number 1120.  ... 
arXiv:2110.13980v1 fatcat:e373if4sszed7i4owzwiabmzxu


Pradheep. T
2018 International Journal of Advanced Research in Computer Science  
Most of the existing cyberbullying methods involves only text detection and few methods are available for analysing the visual detection.  ...  In this proposed work is going to detect multimodel cyberbullying such as audio, video, image along with text in the social networks.  ...  [ 1 ] 1 SheebaJ.I and A.Habiba, "Sentence Abusive Detection using Text Mining."  ... 
doi:10.26483/ijarcs.v9i3.6009 fatcat:mmzwfq65lrepbnfly4g6ri4d3a
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