Login
Create Account

SENTIMENT ANALYSIS USING STRING TOKEN CLASSIFICATION ALGORITHM

Subramaniyaswamy, V, Harshaa, S, Padma Janani, M and Prabhalammbeka, BS SENTIMENT ANALYSIS USING STRING TOKEN CLASSIFICATION ALGORITHM. International Journal of Pure and Applied Mathematics, 2018, vol. 119, n. 12, pp. 13287-13295. [Journal article (Paginated)]

Preview

Text
1211.pdf
Download (524kB) | Preview

English abstract

Sentiment analysis is a type of data mining which involves computation of opinions, sentiments and to determine if an information or a piece of text conveys positive, negative or neutral opinion. Public opinion regarding various aspects can be found using sentiment analysis. Clustering and classification are the key techniques in sentiment analysis. Consensus clustering is better than existing clustering algorithms as it provides a stable and efficient final result. However, it has its own drawbacks. Instead of performing consensus clustering and selecting classifiers from the consolidated result, we try to develop a new classification algorithm in our work

Item type:	Journal article (Paginated)
Keywords:	Consensus Clustering, Sentiment Analysis, String Token Classifier, Text Classification
Subjects:	B. Information use and sociology of information B. Information use and sociology of information > BC. Information in society.
Depositing user:	Raster Daster
Date deposited:	02 Aug 2018 07:36
Last modified:	02 Aug 2018 07:36
URI:	http://hdl.handle.net/10760/33269

Check full metadata for this record

References

[1]. Agarwal, S., Pandey, G. N., & Tiwari, M. D. (2012). Data mining in education: data classification and decision tree approach. International Journal of e-Education, e-Business, e-Management and e-Learning, 2(2),140.

[2]. Chen, L., Wang, W., Nagarajan, M., Wang, S., & Sheth, A. P. (2012). Extracting diverse sentiment expressions with target-dependent polarity from Twitter. In Proceedings of the sixth international AAAI conference on weblogs and social media (pp. 50–57).

[3].Elghazel, H. , Aussem, A. , Gharroudi, O. , & Saadaoui, W. (2016). Ensemble multi-label text categorization based on rotation forest and latent semantic index- ing. Expert Systems with Applications, 57, 1–11.

[4].Fusilier, D. H. , Montes-y-Gomez, M. , Rosso, P. , & Cabrera, R. G. (2015). Detecting positive and negative deceptive opinions using PU-learning. Information Processing and Management, 51, 433–443.

[5]. Korde, V., & Mahender, C. N. (2012). Text classification and classifiers: A survey. International Journal of Artificial Intelligence & Applications, 3(2),85.

[6]. Khatri, M. (2012). A survey of naïve bayesian algorithms for similarity in recommendation systems. International Journal of Advanced Research in Computer Science and Software Engineering, 2(5).

[7]. Lee, J., Sun, M., & Lebanon, G. (2012). A comparative study of collaborative filtering algorithms. arXiv preprint arXiv:1205.3193.

[8].Mendialdua, I. , Arruti, A. , Jauregi, E. , Lazkano, E. , & Sierra, B. (2015). Classifier subset selection to construct multi-classifiers by means of estimation of distribution algorithms. Neurocomputing, 157, 46–60.

[9]. Parimala, C., & Porkodi, R. (2018). Classification Algorithms in Data Mining: A Survey.

[10]. Parashar, H. J., Vijendra, S., & Vasudeva, N. (2012). An efficient classification approach for data mining. International Journal of Machine Learning and Computing, 2(4), 446.

[11]. Patel, B., & Gondaliya, C. (2017). Student Performance Analysis Using Data Mining Technique.

[12]. Ponmani, S., & Vidhu Priya, P. (2017). Classification Algorithms in Data Mining–A Survey. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume, 6.

[13]. Ting, S. L., Ip, W. H., & Tsang, A. H. (2011). Is Naive Bayes a good classifier for document classification. International Journal of Software Engineering and Its Applications, 5(3), 37-46.

[14]. WEKA 3: (2016). Data mining software in Java (n.d.) Retrieved May 25 from http://www.cs.waikato.ac.nz/ml/weka/.

[15]. Zhao, L., Chen, D. F., Xu, S. J., & Lu, J. (2015). The Research of Data Mining Classification Algorithm that Based on SJEP. International Journal of Database Theory and Application, 8(2), 223-234.

[16]. Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., Gao, X. Z., & Indragandhi, V. (2017). A hybrid quantum-induced swarm intelligence clustering for the urban trip recommendation in smart city. Future Generation Computer Systems, 83, 653-673.

[17]. Subramaniyaswamy, V., & Logesh, R. (2017). Adaptive KNN based Recommender System through Mining of User Preferences. Wireless Personal Communications, 97(2), 2229-2247.

[18]. Logesh, R., & Subramaniyaswamy, V. (2017). A Reliable Point of Interest Recommendation based on Trust Relevancy between Users. Wireless Personal Communications, 97(2), 2751-2780.

[19]. Logesh, R., & Subramaniyaswamy, V. (2017). Learning Recency and Inferring Associations in Location Based Social Network for Emotion Induced Point-of-Interest Recommendation. Journal of Information Science & Engineering, 33(6), 1629–1647.

[20]. Subramaniyaswamy, V., Logesh, R., Abejith, M., Umasankar, S., & Umamakeswari, A. (2017). Sentiment Analysis of Tweets for Estimating Criticality and Security of Events. Journal of Organizational and End User Computing (JOEUC), 29(4), 51-71.

[21]. Indragandhi, V., Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., Siarry, P., & Uden, L. (2018). Multi-objective optimization and energy management in renewable based AC/DC microgrid. Computers & Electrical Engineering.

[22]. Subramaniyaswamy, V., Manogaran, G., Logesh, R., Vijayakumar, V., Chilamkurti, N., Malathi, D., & Senthilselvan, N. (2018). An ontology-driven personalized food recommendation in IoT-based healthcare system. The Journal of Supercomputing, 1-33.

[23]. Arunkumar, S., Subramaniyaswamy, V., & Logesh, R. (2018). Hybrid Transform based Adaptive Steganography Scheme using Support Vector Machine for Cloud Storage. Cluster Computing.

[24]. Indragandhi, V., Subramaniyaswamy, V., & Logesh, R. (2017). Resources, configurations, and soft computing techniques for power management and control of PV/wind hybrid system. Renewable and Sustainable Energy Reviews, 69, 129-143.

[25]. Ravi, L., & Vairavasundaram, S. (2016). A collaborative location based travel recommendation system through enhanced rating prediction for the group of users. Computational intelligence and neuroscience, 2016, Article ID: 1291358.

[26]. Logesh, R., Subramaniyaswamy, V., Malathi, D., Senthilselvan, N., Sasikumar, A., & Saravanan, P. (2017). Dynamic particle swarm optimization for personalized recommender system based on electroencephalography feedback. Biomedical Research, 28(13), 5646-5650.

[27]. Arunkumar, S., Subramaniyaswamy, V., Karthikeyan, B., Saravanan, P., & Logesh, R. (2018). Meta-data based secret image sharing application for different sized biomedical images. Biomedical Research,29.

[28]. Vairavasundaram, S., Varadharajan, V., Vairavasundaram, I., & Ravi, L. (2015). Data mining‐based tag recommendation system: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(3), 87-112.

[29]. Logesh, R., Subramaniyaswamy, V., & Vijayakumar, V. (2018). A personalised travel recommender system utilising social network profile and accurate GPS data. Electronic Government, an International Journal, 14(1), 90-113.

[30]. Vijayakumar, V., Subramaniyaswamy, V., Logesh, R., & Sivapathi, A. (2018). Effective Knowledge Based Recommeder System for Tailored Multiple Point of Interest Recommendation. International Journal of Web Portals.

[31]. Subramaniyaswamy, V., Logesh, R., & Indragandhi, V. (2018). Intelligent sports commentary recommendation system for individual cricket players. International Journal of Advanced Intelligence Paradigms, 10(1-2), 103-117.

[32]. Indragandhi, V., Subramaniyaswamy, V., & Logesh, R. (2017). Topological review and analysis of DC-DC boost converters. Journal of Engineering Science and Technology, 12 (6), 1541–1567.

[33]. Saravanan, P., Arunkumar, S., Subramaniyaswamy, V., & Logesh, R. (2017). Enhanced web caching using bloom filter for local area networks. International Journal of Mechanical Engineering and Technology, 8(8), 211-217.

[34]. Arunkumar, S., Subramaniyaswamy, V., Devika, R., & Logesh, R. (2017). Generating visually meaningful encrypted image using image splitting technique. International Journal of Mechanical Engineering and Technology, 8(8), 361–368.

[35]. Subramaniyaswamy, V., Logesh, R., Chandrashekhar, M., Challa, A., & Vijayakumar, V. (2017). A personalised movie recommendation system based on collaborative filtering. International Journal of High Performance Computing and Networking, 10(1-2), 54-63.

[36]. Senthilselvan, N., Udaya Sree, N., Medini, T., Subhakari Mounika, G., Subramaniyaswamy, V., Sivaramakrishnan, N., & Logesh, R. (2017). Keyword-aware recommender system based on user demographic attributes. International Journal of Mechanical Engineering and Technology, 8(8), 1466-1476.

[37]. Subramaniyaswamy, V., Logesh, R., Vijayakumar, V., & Indragandhi, V. (2015). Automated Message Filtering System in Online Social Network. Procedia Computer Science, 50, 466-475.

[38]. Subramaniyaswamy, V., Vijayakumar, V., Logesh, R., & Indragandhi, V. (2015). Unstructured data analysis on big data using map reduce. Procedia Computer Science, 50, 456-465.

[39]. Subramaniyaswamy, V., Vijayakumar, V., Logesh, R., & Indragandhi, V. (2015). Intelligent travel recommendation system by mining attributes from community contributed photos. Procedia Computer Science, 50, 447-455.

[40]. Vairavasundaram, S., & Logesh, R. (2017). Applying Semantic Relations for Automatic Topic Ontology Construction. Developments and Trends in Intelligent Technologies and Smart Systems, 48.

Downloads

Downloads per month over past year

Actions (login required)

View Item

Facebook

Twitter

RSS