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
Mary Vennila, S.
- Discovering Positive Association of ASD Attributes With Class Using Multi Objective Cultural Algorithm
Abstract Views :193 |
PDF Views:0
Authors
R. Abitha
1,
S. Mary Vennila
1
Affiliations
1 Department of Computer Science, Presidency College, IN
1 Department of Computer Science, Presidency College, IN
Source
ICTACT Journal on Soft Computing, Vol 11, No 1 (2020), Pagination: 2222-2226Abstract
Association rule mining (ARM) is a common and most preferable research method for bringing out the fascinating relations between the variables provided in any data set. It brings out the knowledge by satisfying the user defined values and criteria measures specified by the researcher. Frequent item set generation is a well-known method carried out by many researchers to retrieve interesting correlations among the variables that helps in decision making. The accuracy of the brought out rules by ARM is good enough to provide a conclusion on research studies. This can be improved by incorporating optimization like heuristic search techniques. In this paper cultural algorithm is used to improve the performance of rule mining by optimization which is required for categorizing the risk level of ASD individuals. Optimization is utilized in health care domain for generating optimized rules to analyze the frequently combined attributes among the patient’s data. It gracefully improves the result finding process which will be tranquil to conclude the decision. The Cultural algorithm fit in to the larger course of evolutionary algorithms that is inspired by natural evolution. In this paper multi objective optimization technique is proposed by incorporating ARM and cultural algorithm by considering different objectives namely support, confidence, lift and completeness of the rule to find the positive association of Autism Spectrum Disorder (ASD) screening data features with positive class. The result of this research depicts the positive association with improved performance along with reduced number of rules.Keywords
Association Rule Mining, ASD, Apriori Algorithm, Rule Generation, Cultural Algorithm, Risk Level, Optimization.- Deep CNN with SVM-Hybrid Model for Sentence-Based Document Level Sentiment Analysis Using Subjectivity Detection
Abstract Views :169 |
PDF Views:1
Authors
K. Raviya
1,
S. Mary Vennila
1
Affiliations
1 PG and Research Department of Computer Science, Presidency College, IN
1 PG and Research Department of Computer Science, Presidency College, IN
Source
ICTACT Journal on Soft Computing, Vol 11, No 3 (2021), Pagination: 2344-2452Abstract
With the growth of e-commerce reporting, online customer reviews have evolved rapidly, voicing the sentiment or opinion of customers about goods. The analysis of belief could provide useful data for us. Sentiment analysis on social media like Twitter or Facebook, is now the comprehensive way of understanding about the views of customers and has extensive variety of applications. In the context of NLP, automated text classification can be a fundamental activity and it can help people to pick essential information from vast text resources. Sentiment analysis may be a computational technique that plays a key role in automating the retrieval of subjective knowledge, i.e. customer’s sentiment from online text reviews or opinion from social network like Twitter and Facebook. Lexicon-based and machine learning-based methods are two main approaches widely used in sentiment analysis activities. In machine learning based framework, Sentiment analysis is a text recognition task. The outcome depends not only from the soundness of the algorithm for machine learning, but also with the appropriate features. In recent years, the most recent technological advancements, like deep-learning techniques, have resolved a number of standard challenges caused by the lack of lexical tools in the region. It has been exhibited that deep-learning models are auspicious and potential tool to NLP challenges. In this work, the fusion of deep CNN with SVM will automatically detect and extract subjective sentence-level features to perform sentiment analysis of online product review dataset with highest accuracy and less computation time.Keywords
Sentiment Analysis, Deep Learning, Convolutional Neural Network, Bigdata.References
- Shahid Shayaa and Noor Ismawati,” Sentiment Analysis of Big Data: Methods, Applications and Open Challenges”, IEEE Access, Vol. 6, pp. 1-13, 2018.
- Y. Kim, “Convolutional Neural Networks for Sentence Classification”, Proceedings of International Conference on Computation and Language, pp. 1-13, 2014.
- S. Xing, Q. Wang and T. Li, “A Hierarchical Attention Model for Rating Prediction by Leveraging User and Product Reviews”, Neurocomputing, Vol. 332, pp. 417-427, 2019.]
- A. Sun and Y. Liu, “On Strategies for Imbalanced Text Classification using SVM: A Comparative Study”, Decision Support Systems, Vol. 481, pp. 191-201, 2010.
- A. Kennedy and D. Inkpen, “Sentiment Classification of Movie Reviews using Contextual Valence Shifters”, Computational Intelligence, Vol. 22, No. 2, pp. 110-125, 2006.
- G. Gautam and D. Yadav, “Sentiment Analysis of Twitter Data using Machine Learning Approaches Semantic Analysis”, Proceedings of 7th International Conference on Contemporary Computing, pp. 437-442, 2014.
- A. Tripathy, A. Agrawal and S.K. Rath, “Classification of Sentiment Reviews using N-Gram Machine Learning Approach”, Expert Systems and Applications, Vol. 57, pp. 117-126, 2016.
- S.A. Bahrainian and A. Dengel, “Sentiment Analysis using Sentiment Features”, Proceedings of IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies, pp. 26-27, 2013.
- M. Neethu and R. Rajasree, “Sentiment Analysis in Twitter using Machine Learning Techniques”, Proceedings of International Conference on Computing, Communications and Networking Technologies, pp. 1-5, 2013.
- Nadia Nedjah, Igor Santos and Luiza De Macedo Mourelle, “Sentiment Analysis using Convolutional Neural Network Via Word Embeddings”, Evolutionary Intelligence, Vol. 6, No. 2, pp. 1-13, 2019.
- Shiyang Liao, Junbo Wang and Ruiyun Yu, “CNN for Situations Understanding based on Sentiment Analysis of Twitter Data”, Proceedings of International Conference on Advances in Information Technology, pp. 19-22, 2016.
- A. Rozental and D. Fleischer, “Task 1: GRU Neural Network with a CNN Attention Mechanism for Sentiment Classification”, Proceedings of International Workshop on Semantic Evaluation, pp. 1-6, 2018.
- A. Kamal, “Subjectivity Classification using Machine Learning Techniques for Mining Feature-Opinion Pairs from Web Opinion Sources”, Proceedings of International Conference on Computing, Communications and Networking Technologies, pp. 332-335, 2013.
- B. Pang, Lillian Lee and Shivakumar Vaithyanathan, “Thumbs Up? Sentiment Classification using Machine Learning Techniques”, Proceedings of International Conference on Empirical Methods in Natural Language, pp. 79-86, 2002.
- Hong Yu and Vasileios Hatzivassiloglou, “Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences”, Proceedings of International Conference on Empirical Methods in Natural Language, pp. 132-137, 2002.
- Trevor Hastie, Christopher Manning and Shivakumar Vaithyanathan, “Exploring Sentiment Summarization”, Proceedings of AAAI Spring Symposium on Exploring Attitude and Affect in Text: and Applications Theories, pp. 1-7, 2004.
- A. Krizhevsky, I. Sutskever and G.E. Hinton, “Imagenet Classification with Deep Convolutional Neural Networks”, Proceedings of International Conference on Neural Information Processing Systems, pp. 1106-1114, 2012.
- A. Graves, A.R. Mohamed and G. Hinton, “Speech Recognition with Deep Recurrent Neural Networks”, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645-6649, 2013.
- T. Mikolov, I. Sutskever and K. Chen, “Distributed Representations of Words and Phrases and their Compositionality”, Proceedings of the IEEE International Conference on Neural Information Processing Systems, pp. 3111-3119, 2013.
- Jupyter, Available at http://jupyter.org/, Accessed at 2020.
- TensorFlow, Available at https://www.tensorflow.org/, Accessed at 2020.