Refine your search
Collections
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Sheeba, J. I.
- Analysis of Different Similarity Functions with Fuzzy C-Means Clustering Approach Using Meeting Transcripts
Abstract Views :177 |
PDF Views:5
Authors
Affiliations
1 Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry-605014, IN
1 Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry-605014, IN
Source
Data Mining and Knowledge Engineering, Vol 6, No 7 (2014), Pagination: 311-315Abstract
Clustering is a technique of automatically grouping similar data into clusters. A large diversity of similarity measures distance functions such as Euclidean distance, Jaccard distance, Pearson Correlation distance, Cosine similarity and Kullback-Leibler Divergence have been implemented for clustering. Fuzzy C means algorithm is implemented for assigning membership to each word point in the cluster. In the same way it is calculated to each cluster center from the origin of remote region between the cluster center and the word point in this process. This proposed framework is used to validate the five similarity measure functions with Fuzzy C means clustering algorithm for finding the effectiveness. To estimate the optimal number of clusters, by implementing the validity measures like purity and entropy. Finally the results are compared five similarity measure functions with Fuzzy C Means clustering algorithm. Euclidean similarity measure function provides better and accurate results as compared to the other distance functions.Keywords
Clustering, Euclidean Distance, Fuzzy C Means Algorithm, Similarity Measure.
- Sentence Abusive Detection Using Text Mining
Abstract Views :166 |
PDF Views:3
Authors
A. Habiba
1,
J. I. Sheeba
2
Affiliations
1 Department of Computer Science and Engineering, Alpha College of Engineering, Puducherry, IN
2 Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, IN
1 Department of Computer Science and Engineering, Alpha College of Engineering, Puducherry, IN
2 Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 7 (2013), Pagination: 288-291Abstract
Text mining is the significant aspects of study and research motivated by the remarkable growth of the social web. Cyber bully is a major problem that occurs in the online communications. Nowadays, methods for automatic thoughts mining on online data are becoming increasingly important. In this proposal, we studied about the detection of the general cyber bully polarity. A thorough analysis has been made for sentiment (i.e.) opinion mining but the cyber bullies which harass and threatens the online social victims research work has not been done as familiar. The aim of this paper is to extract cyber bully polarity from blog messages with methods of pre-processing, frequency measure and Classifier algorithm. The novelty of this paper arise from treating user generated content on blogs as dynamically evolving linked documents that vary by thoughts, content and emotions.Keywords
Text Mining, Cyber Bully, Classification, Classifier Algorithm, Abusive Detection.- Cyberbully Detection from Twitter Using Classifiers
Abstract Views :187 |
PDF Views:6
Authors
Affiliations
1 Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry-605014, IN
2 School of Mechanical and Building Sciences, Christ College of Engineering and Technology, IN
1 Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry-605014, IN
2 School of Mechanical and Building Sciences, Christ College of Engineering and Technology, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 9, No 8 (2017), Pagination: 163-168Abstract
These days social communication networks become a part of the daily activity and the users of the social media also increased. The increasing use of social networks by their users leads to large amount of user communication data. And the popularity of social media causes cyberbullying and it became the major problem in communication through online. Cyberbullying leads to many severe problems, undesirable effect on human’s life and it also lead to suicides. In the existing system the unique information such as network, activity, user and tweet contents are extracted from Twitter. By the use of extracted information the cyberbullying words present in the tweet contents are detected using machine learning algorithms like Naïve Bayes, Random Forest, Support Vector Machine and KNN. In the proposed work the rumor tweets and cyberbully tweets are detected, along with these the cyberbully words in the tweet comments also detected using Random Forest and Naïve Bayes classifiers. The required information’s such as name, gender and age of the cyberbully tweeted persons are detected. By the use of twitter speech act classification features along with the machine learning classifiers, the rumor tweets are detected in this proposed work.
Keywords
Cyberbullying Detection, Data Preprocessing, Machine Learning Algorithms, Twitter, Feature Extraction, Rumor Detection.References
- A. Saravanaraj, J. I. Sheeba, S. Pradeep Devaneyan, Automatic Detection of Cyberbullying from Twitter, ISSN: 2249-9555 Vol.6, No.6, Nov-Dec 2016, pp. 26-31, IJCSITS.
- Rui Zhao, Anna Zhou, Kezhi Mao, Automatic Detection of Cyberbullying on Social Networks based on Bullying Features, ICDCN ’16 Article No. 43, January 2016, ACM.
- Chen, Ying, Yilu Zhou, Sencun Zhu, and Heng Xu. "Detecting offensive language in social media to protect adolescent online safety." In Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Conference on Social Computing (SocialCom), pp. 71-80. IEEE, 2012.
- Qiao Zhang, Shuiyuan Zhang, Jian Dong, Jinhua Xiong, and Xueqi Cheng. Automatic Detection of Rumor on Social Network, pp. 113–122, Springer (2015).
- SardarHamidian and Mona Diab. Rumor Detection and Classification for Twitter Data, IARIA (2015), 71-77, SOTICS 2015: The Fifth International Conference on Social Media Technologies, Communication, and Informatics, ISBN: 978-1-61208-443-5.
- Dadvar, M., Trieschnigg, D., Ordelman, R., & de Jong, F. (2013). Improving cyberbullying detection with user context. In Advances in information retrieval (pp. 693-696). Springer.
- Mohammed Ali Al-garadi, KasturiDewiVarathan, Sri Devi Ravana. Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network, Computers in Human Behavior 63 (2016) 433-443, Elsevier.
- Nalini, K., & Sheela, L. J. (2015). Classification of Tweets using text classifier to detect cyber bullying. In Emerging ICT for bridging the future-Proceedings of the 49th Annual convention of the Computer Society of India CSI (Vol. 2, pp. 637-645). Springer.
- Chavan, V. S., &Shylaja, S. (2015). Machine learning approach for detection of cyber aggressive comments by peers on social media network. In Advances in computing, communications and informatics (ICACCI), 2015 International Conference on (pp. 2354-2358). IEEE.
- J.I. Sheeba and A.Habiba, "Sentence Abusive Detection using Text Mining." CiiT International Journal of Data Mining and Knowledge Engineering, Vol 5. No.7, ISSN (Online): 0974 – 9578, ISSN (Print):0974-9683, 2013 pp: 288-291, 2013.
- J.I.Sheeba and K.Vivekanandan, “Analysis Of Different Similarity Functions with Fuzzy C-Means Clustering Approach Using Meeting Transcripts”, CiiT International Journal of Data Mining and Knowledge Engineering, Vol. 6, No7, ISSN (Online): 0974 – 9578,ISSN (Print):0974-9683, pp.311-315, October 2014
- X. Zhao and J. Jiang. An empirical comparison of topics in twitter and traditional media. Singapore Management University School of Information Systems Technical paper series. Retrieved November, 10:2011, 2011.
- Vosoughi, Soroush, and Deb Roy. Tweet acts: A speech act classifier for twitter. Ar Xiv preprint arXiv: 1605. 05156 (2016).
- Sanchez, Huascar, and Shreyas Kumar. "Twitter bullying detection." ser. NSDI 12 (2011): 15-15.
- B.Sri Nandhini, J.I.Sheeba,: Cyberbullying Detection and Classification Using Information Retrieval Algorithm. In: ICARCSET '15, ACM, pp.1-5 (2015)
- B.Sri Nandhini, J.I.Sheeba,: Online Social Network Bullying Detection Using Intelligence Techniques. In: Procedia Computer Science 45 (2015) 485 – 492, Elsevier, pp.1-8 (2015)
- J.I.Sheeba, K.Vivekanandan,: Detection of Online Social Cruelty Attack from Forums. In: International InJournal of Data Mining and Emerging Technologies DOI: 10.5958/2249- 3220.2014.00003.2, IndianJournals.com, pp.1-11 ( 2015)
- Sheeba,J. I., & Devaneyan, S. P.. : Cyberbully Detection Using Intelligent Techniques. International Journal of Data Mining And Emerging Technologies, 6(2), pp. 86-94 (2016)
- Tata Prathyusha, R. Hemavathy and Dr.J.I.Sheeba,: Cyberbully Detection Using Hybrid Techniques. In: International Conference on Telecommunication, Power Analysis and Computing Techniques (ICTPACT -2017) IEEE, pp.1-6 (2017)
- Cyberbully Image and Text Detection using Convolutional Neural Networks
Abstract Views :196 |
PDF Views:0
Authors
Affiliations
1 Pondicherry Engineering College., IN
2 Department of Computer Science and Engineering, Pondicherry Engineering College, IN
3 Christ College of Engineering and Technology, Puducherry, IN
1 Pondicherry Engineering College., IN
2 Department of Computer Science and Engineering, Pondicherry Engineering College, IN
3 Christ College of Engineering and Technology, Puducherry, IN
Source
Fuzzy Systems, Vol 11, No 2 (2019), Pagination: 25-30Abstract
Social media is getting more and more popular in our day to day life. The popularity of social media affects the people involved in it. This makes the technology to do the work or to feel smarter and but only makes us lazy. Therefore, in this robust, discriminative and numerical representation, learning of text messages is a critical issue. Hence, the existing system helps to detect the cyberbully words using Naive Bayes Classifier. The output is classified into cyberbully and not cyberbully words from the Instagram dataset and accuracy is calculated. The proposed framework deployed for detecting negative online interactions in terms of abusive contents carried out through both text and images. This proposed technique is going to detect the cyberbully image and text on the Instagram dataset using Convolutional Neural Network and Bag of words techniques along with the existing technique. Thus, the detected cyberbully words are further classified using Naive Bayes classifier such as Harassing, Insulting, Trolling and Threatening. The combination of text & image analysis techniques is considered an appropriate platform for the detection of potential cyberbully threats.
Keywords
Bag of Words, Convolutional Neural Networks, Cyberbully Image, Cyberbully Words, Instagram Dataset, Naive Bayes Classifier.References
- . Rui Zhao, Kezhi Mao, “CyberBullying based on Semantic Enhanced Marginalized Denoising Auto Encoder”, IEEE Transactions on Affective Computing, ISSN 1949-3045, pp.1-12, (2016).
- . M.Devi M. Chitra Devi “Fuzzy-Based Genetic Operators for CyberBullying Detection Using Social Network Data”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Issue 4, ISSN 2456-3307, vol 3,pp.437-444, (2013).
- . B.Sri Nandhini, J.I.Sheeba, “Cyberbullying Detection and Classification Using Information Retrieval Algorithm”, In ICARCSET '15, ACM, pp.1-5, (2015).
- . B.Sri Nandhini, J.I.Sheeba, “Online Social Network Bullying Detection Using Intelligence Techniques”, In Procedia Computer Science 45, 485 – 492, Elsevier, pp.1-8, (2015).
- . J.I.Sheeba, K.Vivekanandan, “Detection of Online Social Cruelty Attack from Forums”, International Journal of Data Mining and Emerging Technologies,DOI: 10.5958/2249- 3220.2014.00003.2, IndianJournals.com, pp.1-11, (2015).
- . A.Saravanaraj, J.ISheeba and S.Pradeep Devaneyan, Automatic Detection of Cyberbullying from Twitter”, International Journal of Computer Science and Information Technology & Security (IJCSITS) Vol.6 No.6, ISSN: 2249-9555, pp.1-6, (2016).
- . Nafsika Antoniadou, Constantinos M. Kokkinos, and Angelos Markos, “Possible common correlates between bullying and cyber-bullying among adolescents”, In Psicologia Educativa, Elsevier, 22:27-38, pp.1-12, (2016).
- . Hariani, Imam Riadi, “Detection of Cyberbullying on Social Media Using Data Mining Techniques”, International Journal of Computer Science and Information Security (IJCSIS), ISSN 1947-5500 vol 15, No. 3, pp: 244-250, (2017).
- . Krishna B.Kansara and Narendra M.Shekokar, “A Framework for Cyberbullying Detection in Social network”, International Journal of Current Engineering and Technology, P-ISSN 2347 – 5161, vol 5, No.1 F, pp. 494-498, (2015).
- . [10] Paridhi Shingal and Ashish Bansal, “Improved Textual cyberbullying detection using Data Mining”, International Journal of Computer Science and Computation Technology, ISSN 0974-2239, vol 3, no.6, pp. 569-576, (2013).
- . Michele Di Capua, Emanuel Di Nardo, and Alfredo Petrosino, “Unsupervised Cyber Bullying Detection in Social Networks”, In 2016 23rd International Conference on Pattern Recognition (ICPR), IEEE, pp. 1-6, (2016).
- . Zhao, R., Zhou, A.and Mao, K., “ Automatic detection of cyberbullying on social networks based on bullying features”, In 17th International Conference on Distributed Computing and Networking, ACM, pp.43, (2016).
- . Abdelhaq, H., Gertz, M. and Armiti, A., “Efficient online extraction of keywords for localized events in Twitter”, In GeoInformatica, pp.1-24, (2016).
- . Dornaika, F., El Traboulsi, Y., and Assoum, A. “Inductive and flexible feature extraction for semi-supervised pattern categorization”. Pattern Recognition, 60, pp. 275-285, (2016).
- . T.Guo, J. Dong, H. Li and Y. Gao, "Simple convolutional neural network on image classification," 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), pp. 721-724, (2017).