A roadmap towards implementing parallel aspect level sentiment analysis

[1]  Bige Tunçer,et al.  Harnessing Multi-Source Data about Public Sentiments and Activities for Informed Design , 2019, IEEE Transactions on Knowledge and Data Engineering.

[2]  Raymond Y. K. Lau,et al.  Parallel Aspect‐Oriented Sentiment Analysis for Sales Forecasting with Big Data , 2018 .

[3]  Dharmendra Singh Rajput,et al.  Evaluating the Performance of Supervised Classification Models: Decision Tree and Naïve Bayes Using KNIME , 2018, International journal of engineering and technology.

[4]  Huan Wang,et al.  New cubic reference table based image steganography , 2018, Multimedia Tools and Applications.

[5]  Dharmendra Singh Rajput,et al.  Impact of Gradient Ascent and Boosting Algorithm in Classification , 2018 .

[6]  Dharmendra Singh Rajput,et al.  Evaluating the Impact of Feature Selection on Overall Performance of Sentiment Analysis , 2017, ICIT 2017.

[7]  Mostafa A. Bassiouni,et al.  Improving MapReduce Performance with Progress and Feedback Based Speculative Execution , 2017, 2017 IEEE International Conference on Smart Cloud (SmartCloud).

[8]  Zheng Qin,et al.  Data embedding in digital images using critical functions , 2017, Signal Process. Image Commun..

[9]  Mirjam Moerbeek,et al.  Multilevel Analysis : Techniques and Applications, Third Edition , 2017 .

[10]  John Mylopoulos,et al.  Finding Related Forum Posts through Content Similarity over Intention-Based Segmentation , 2017, IEEE Transactions on Knowledge and Data Engineering.

[11]  Mohammad Soleymani,et al.  A survey of multimodal sentiment analysis , 2017, Image Vis. Comput..

[12]  Chunyan Miao,et al.  Analyzing Sentiments in One Go: A Supervised Joint Topic Modeling Approach , 2017, IEEE Transactions on Knowledge and Data Engineering.

[13]  Jill Mosteller,et al.  Online Reviewer Engagement , 2017 .

[14]  Anthony J. Weiss,et al.  Expectation-maximization algorithm for direct position determination , 2017, Signal Process..

[15]  Stuart J. Barnes,et al.  Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation , 2017 .

[16]  Youngsam Kim,et al.  Finding Sentiment Dimension in Vector Space of Movie Reviews: An Unsupervised Approach , 2017 .

[17]  N. McGranahan,et al.  Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future , 2017, Cell.

[18]  Bige Tunçer,et al.  Exploring the utilization of places through a scalable “Activities in Places” analysis mechanism , 2016, 2016 IEEE International Conference on Big Data (Big Data).

[19]  Wenyuan Wu,et al.  Structural Analysis Methods for Differential Algebraic Equations via Fixed-Point Iteration , 2016 .

[20]  Bige Tunçer,et al.  Exploring public sentiments for livable places based on a crowd-calibrated sentiment analysis mechanism , 2016, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[21]  Dimitris Spathis,et al.  A comparison between semi-supervised and supervised text mining techniques on detecting irony in greek political tweets , 2016, Eng. Appl. Artif. Intell..

[22]  Christophe Dupuy,et al.  Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling , 2016, J. Mach. Learn. Res..

[23]  Kim Schouten,et al.  Survey on Aspect-Level Sentiment Analysis , 2016, IEEE Transactions on Knowledge and Data Engineering.

[24]  Pushpak Bhattacharyya,et al.  Automatic Sarcasm Detection , 2016, ACM Comput. Surv..

[25]  Peng Wang,et al.  Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification , 2016, Neurocomputing.

[26]  Christopher D. Manning,et al.  Advances in natural language processing , 2015, Science.

[27]  David Bamman,et al.  Contextualized Sarcasm Detection on Twitter , 2015, ICWSM.

[28]  Reza Zafarani,et al.  Sarcasm Detection on Twitter: A Behavioral Modeling Approach , 2015, WSDM.

[29]  Tomoko Ohkuma,et al.  TeamX: A Sentiment Analyzer with Enhanced Lexicon Mapping and Weighting Scheme for Unbalanced Data , 2014, *SEMEVAL.

[30]  Gao Cong,et al.  Coarse-to-fine review selection via supervised joint aspect and sentiment model , 2014, SIGIR.

[31]  Philipp Cimiano,et al.  An Impact Analysis of Features in a Classification Approach to Irony Detection in Product Reviews , 2014, WASSA@ACL.

[32]  Christopher C. Yang,et al.  Identifying Features in Opinion Mining via Intrinsic and Extrinsic Domain Relevance , 2014, IEEE Transactions on Knowledge and Data Engineering.

[33]  Paolo Rosso,et al.  A multidimensional approach for detecting irony in Twitter , 2013, Lang. Resour. Evaluation.

[34]  Andrew N. Smith,et al.  How Does Brand-related User-generated Content Differ across YouTube, Facebook, and Twitter? , 2012 .

[35]  ChengXiang Zhai,et al.  Opinion-based entity ranking , 2012, Information Retrieval.

[36]  Brett Hutchins,et al.  THE ACCELERATION OF MEDIA SPORT CULTURE , 2011 .

[37]  Adrian Perrig,et al.  ACE: An Emergent Algorithm for Highly Uniform Cluster Formation , 2004, EWSN.

[38]  Timothy J. Robinson,et al.  Multilevel Analysis: Techniques and Applications , 2002 .

[39]  S. Venkatesh,et al.  A steady-state throughput analysis of cluster tools: dual-blade versus single-blade robots , 1997 .

[40]  A. Karmiloff-Smith.  Beyond Modularity: A Developmental Perspective on Cognitive Science , 1992 .

[41]  O. N. Garcia,et al.  Knowledge and Data Engineering: An Outlook , 1989 .

[42]  Dharmendra Singh Rajput,et al.  Conceptual Approach to Predict Loan Defaults Using Decision Trees , 2019, Advances in Business Information Systems and Analytics.

[43]  Yang Wang,et al.  Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths , 2017, IEEE Transactions on Visualization and Computer Graphics.

[44]  Syed Hassan Ahmed,et al.  Named-Data-Networking-Based ITS for Smart Cities , 2017, IEEE Communications Magazine.

[45]  Jorge A. Balazs,et al.  Opinion Mining and Information Fusion: A survey , 2016, Inf. Fusion.

[46]  Harith Alani,et al.  Contextual semantics for sentiment analysis of Twitter , 2016, Inf. Process. Manag..

[47]  Zhendong Niu,et al.  Automatic construction of domain-specific sentiment lexicon based on constrained label propagation , 2014, Knowl. Based Syst..

[48]  Durga Toshniwal,et al.  Feature based Summarization of Customers' Reviews of Online Products , 2013, KES.

[49]  A. Kaplan,et al.  Users of the world, unite! The challenges and opportunities of Social Media , 2010 .

[50]  Uri C. Weiser,et al.  Performance, power efficiency and scalability of asymmetric cluster chip multiprocessors , 2006, IEEE Computer Architecture Letters.

[51]  G. A. Marcoulides,et al.  Multilevel Analysis Techniques and Applications , 2002 .