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Sridhar, Rajeswari
- Fuzzy Logic Based Hybrid Recommender of Maximum Yield Crop Using Soil, Weather and Cost
Abstract Views :176 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Anna University, Chennai, IN
1 Department of Computer Science and Engineering, Anna University, Chennai, IN
Source
ICTACT Journal on Soft Computing, Vol 6, No 4 (2016), Pagination: 1261-1269Abstract
Our system is designed to predict best suitable crops for the region of farmer. It also suggests farming strategies for the crops such as mixed cropping, spacing, irrigation, seed treatment, etc. along with fertilizer and pesticide suggestions. This is done based on the historic soil parameters of the region and by predicting cost of crops and weather. The system is based on fuzzy logic which gets input from an Artificial Neural Network (ANN) based weather prediction module. An Agricultural Named Entity Recognition (NER) module is developed using Conditional Random Field (CRF) to extract crop conditions data. Further, cost prediction is done based on Linear Regression equation to aid in ranking the crops recommended. Using this approach we achieved an F-Score of 54% with a precision of 77% thus accounting for the correctness of crop production.Keywords
Fuzzy, Agricultural NER, Crop Recommendation, Weather Prediction, ANN.
- Emotion and Sarcasm Identification of Posts from Facebook Data Using a Hybrid Approach
Abstract Views :220 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, IN
1 Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, IN
Source
ICTACT Journal on Soft Computing, Vol 7, No 2 (2017), Pagination: 1427-1435Abstract
Facebook has become the most important source of news and people's feedback and opinion about almost every daily topic. Facebook represents one of the largest and most dynamic datasets of user generated content. Facebook posts can express opinions on different topics. With this massive amount of information in Facebook, there has to be an automatic tool that can categorize these information based on emotions. The proposed system is to develop a prototype that help to come to an inference about the emotions of the posts namely anger, surprise, happy, fear, sorrow, trust, anticipation and disgust with three sentic levels in each. This helps in better understanding of the posts when compared to the approaches which senses the polarity of the posts and gives just their sentiments i.e., positive, negative or neutral. The posts handling these emotions might be sarcastic too. When detecting sarcasm in social media posts, the various features that are especially inherent to Facebook must be considered with importance.Keywords
Emotion, Sarcasm, Bipartite, Fuzzy, Conflicting Emotion Model.References
- Nadia F.F. da Silva, Eduardo R. Hruschka and Estevam R. Hruschka Jr., “Tweet Sentiment Analysis with Classifier Ensembles”, Decision Support Systems, Vol. 66, pp. 170-179, 2014.
- Ivan Habernal, Tomas Ptacek and Josef Steinberger, “Supervised Sentiment Analysis in Czech Social Media”, Information Processing and Management, Vol. 50, No. 5, pp. 693-707, 2014.
- C. Strapparava and R. Mihalcea, “Learning to Identify Emotions in Text”, Proceedings of the 2008 ACM Symposium on Applied Computing, pp. 1556-1560, 2008.
- Ze-Jing Chuang and Chung-Hsien Wu, “Multi-Modal Emotion Recognition from Speech and Text”, Computational Linguistics and Chinese Language Processing, Vol. 9, No. 2, pp. 45-62, 2004.
- E. Cambria, R. Speer, C. Havasi and A. Hussain, “SenticNet: A Publicly Available Semantic Resource for Opinion Mining”, Commonsense Knowledge: AAAI Fall Symposium, Vol. FS-10-02, pp. 14-18, 2010.
- Vincenzo Loia and Sabrina Senatore, “A Fuzzy-Oriented Sentic Analysis to Capture the Human Emotion in Web-based Content”, Knowledge-Based Systems, Vol. 58, pp. 75-85, 2014.
- Weiyuan Li and Hua Xu, “Text-based Emotion Classification using Emotion Cause Extraction”, Expert Systems with Applications, Vol. 41, No. 4, Part 2, pp. 1742-1749, 2014.
- O. Tsur, D. Davidov and A. Rappoport, “ICWSM-A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews”, Proceedings of the Fourth International Conference on Weblogs and Social Media, pp. 162-169, 2010.
- R. Gonzlez-Ibez, S. Muresan and N. Wacholder, “Identifying Sarcasm in Twitter: A Closer Look”, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers, Vol. 2, pp. 581-586, 2011.
- Raquel Justo, Thomas Corcoran, Stephanie M. Lukin, Marilyn Walker and M. Ines Torres, “Extracting Relevant Knowledge for the Detection of Sarcasm and Nastiness in the Social Web”, Knowledge-Based Systems, Vol. 69, pp. 124-133, 2014.
- Benno Stein and Sven Meyer Zu Eissen, “Document Categorization with MajorClust”, Proceedings of 12th Workshop on Information Technology and Systems, 2002.
- Robert Plutchik, “The Nature of Emotions”, American Scientist, Vol. 89, No. 4, pp. 344-350, 2001.
- Liling Tan, “Pywsd: Python Implementations of Word Sense Disambiguation (WSD) Technologies [Software]”, https://github.com/alvations/pywsd, 2014.
- Steven Bird, Ewan Klein and Edward Loper, “Natural Language Processing with Python”, O’Reilly, 2009.
- Feature Based Community Detection by Extracting Facebook Profile Details
Abstract Views :231 |
PDF Views:3
Authors
Rajeswari Sridhar
1,
Akshaya Kumar
1,
S. Bagawathi Roshini
1,
Ramya Kumar
1,
Sundaresan
1,
Suganthini Chinnasamy
1
Affiliations
1 Department of Computer Science and Engineering, Anna University, Chennai, IN
1 Department of Computer Science and Engineering, Anna University, Chennai, IN
Source
ICTACT Journal on Soft Computing, Vol 8, No 4 (2018), Pagination: 1706-1713Abstract
The rise of social networks had marked the revolution and transformation of human relationships and the information age. Social networks, Facebook in specific, have more than a billion daily active users which means petabytes of data are generated every second and there are so many social interactions occurring simultaneously. Community detection revolves around the study of these social interactions and common interests to derive the most efficient method of communication to specialized groups. Considering a preferred set of features such as the posts, likes, education background and the location of users for an optimal data structure, the selection of significant users for community analysis is implemented with the unique approach to investment score and dynamic threshold allocations for the graph creation. The community detection process focuses on the analysis of cliques and map-overlay. The emphasis on the detection of overlapping communities enhances the analysis of community relationships.Keywords
Community Detection, Data Structure, Link Weights, Influence Metric, Cliques, Map Overlay.References
- B. Pattabiraman et al., “Fast Algorithms for the Maximum Clique Problem on Massive Graphs with Applications to Overlapping Community Detection”, Journal of Internet Mathematics, Vol. 37, No. 1, pp. 156-169 2014.
- Peng Gang Sun, “Weighting Links based on Edge Centrality for Community Detection”, Physica A: Statistical Mechanics and its Applications, Vol. 394, pp. 346-357, 2014.
- Sudheendra Hangal, Diana MacLean, Monica S. Lam and Jeffrey Heer: “All Friends are Not Equal: using Weights in Social Graphs to Improve Search”, Proceedings of International Conference on Social Network Analysis Knowledge Discovery and Data Mining, pp. 356-371, 2010.
- Mohsen Arab and Mohsen Afsharchi, “Community Detection in Social Networks using Hybrid Merging of Sub Communities”, Journal of Network and Computer Applications, Vol. 40, pp. 73-84, 2014.
- Xingqin Qi, Wenliang Tang, Yezhou Wu, Guodong Guo, Eddie Fuller and Cun-Quan Zhang, “Optimal Local Community Detection in Social Networks Based on Density Drop of Subgraphs”, Pattern Recognition Letters, Vol. 36, pp. 46-53, 2014.
- Joseph E. Gonzalez, Reynold S. Xin, Ankur Dave, Daniel Crankshaw, Michael J. Franklin and Ion Stoica, “Graphx: Graph Processing in a Distributed Dataflow Framework”, Proceedings of 11th Usenix Symposium on Operating Systems Design and Implementation, pp. 599-613. 2014.
- W. Fan and A. Yeung, “Similarity between Community Structures of Different Online Social Networks and Its Impact on Underlying Community Detection”, Journal of Communications in Nonlinear Science and Numerical Simulation, Vol. 20, No. 3, pp. 1015-1025, 2015.
- Gnce Orman, Vincent Labatut and Hocine Cherifi, “Comparative Evaluation of Community Detection Algorithms: A Topological Approach”, Journal of Statistical Mechanics: Theory and Experiment, Vol. 2, pp. 802-809, 2012.
- Hans-Peter Kriegel, Thomas Brinkhoff and Ralf Schneider, “An Efficient Map Overlay Algorithm based on Spatial Access Methods and Computational Geometry”, Proceedings of International Workshop on Database Management Systems for Geographical Applications, pp. 194-211, 1991.
- Mark De Berg, Marc Van Kreveld, Otfried Cheong and Mark Overmars, “Computational Geometry: Algorithms and Applications”, 3rd Edition, Springer, 2008.