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Hits 141 – 158 of 158

141
Mining Sentiment Words from Microblogs for Predicting Writer-Reader Emotion Transition
In: http://www.lrec-conf.org/proceedings/lrec2012/pdf/117_Paper.pdf
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142
Fuzzy based Sentiment Analysis of Online Product Reviews using Machine Learning Techniques
In: http://research.ijcaonline.org/volume99/number17/pxc3898243.pdf
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143
IDENTIFYING E-LEARNER’S OPINION USING AUTOMATED SENTIMENT ANALYSIS IN E-LEARNING
In: http://ijret.org/Volumes/V03/I19/IJRET_110319086.pdf
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144
Opinion Mining Techniques on Social Media Data
In: http://research.ijcaonline.org/volume118/number6/pxc3903149.pdf
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145
Modeling Pollyanna Phenomena in Chinese Sentiment Analysis
In: http://aclweb.org/anthology/C/C12/C12-3029.pdf
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146
Trading strategies based on economics blogs sentiment
In: http://www.efmaefm.org/0EFMAMEETINGS/EFMA ANNUAL MEETINGS/2014-Rome/papers/EFMA2014_0502_fullpaper.pdf
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147
Improved Sentiment Classification From Meeting Transcripts J.I.Sheeba 1 and Dr.K.Vivekanandan 2
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148
Sentiment Classification in Resource-Scarce Languages by using Label Propagation
In: http://www.aclweb.org/anthology-new/Y/Y11/Y11-1044.pdf
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149
Predicting Stance in Ideological Debate with Rich Linguistic Knowledge
In: http://aclweb.org/anthology/C/C12/C12-2045.pdf
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150
Sentiment Classification Considering Negation and Contrast Transition ∗ ∗ ∗
In: http://www.aclweb.org/anthology-new/Y/Y09/Y09-1032.pdf
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151
University Seoul 151-742, Republic of Korea
In: http://ids.snu.ac.kr/w/images/7/7e/IC-2008-01.pdf
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152
Feature based Star Rating of Reviews: A Knowledge-Based Approach for Document Sentiment Classification
In: http://www.sersc.org/journals/IJHIT/vol5_no4_2012/10.pdf
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153
Summarization of Movie Reviews in Mobile Environment
In: http://research.ijcaonline.org/volume100/number1/pxc3898008.pdf
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154
Mining and Summarizing Customer Reviews
In: http://www.cs.uic.edu/~liub/publications/kdd04-revSummary.pdf
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155
Cross-domain sentiment classification using grams derived from syntax trees and an adapted naive Bayes approach
Cheeti, Srilaxmi. - : Kansas State University, May
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156
Finding evidence of fraudster companies in the CEO’s letter to shareholders with sentiment analysis
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157
Comparing Supervised Machine Learning Strategies and Linguistic Features to Search for Very Negative Opinions
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158
Corpus-based Construction of Sentiment Lexicon to Identify Extreme Opinions by Supervised and Unsupervised Machine learning Methods
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