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Tweet Recommendation with Graph Co-Ranking
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Yoshinari Fujinuma
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Recommandé
Twitter has brought much attention recently as a hot research topic in the domain of sentiment analysis. Training sentiment classifiers from tweets data often faces the data sparsity problem partly due to the large variety of short and irregular forms introduced to tweets because of the 140-character limit. In this work we propose using two different sets of features to alleviate the data sparseness problem. One is the semantic feature set where we extract semantically hidden concepts from tweets and then incorporate them into classifier training through interpolation. Another is the sentiment-topic feature set where we extract latent topics and the associated topic sentiment from tweets, then augment the original feature space with these sentiment-topics. Experimental results on the Stanford Twitter Sentiment Dataset show that both feature sets outperform the baseline model using unigrams only. Moreover, using semantic features rivals the previously reported best result. Using sentiment-topic features achieves 86.3% sentiment classification accuracy, which outperforms existing approaches.
Alleviating Data Sparsity for Twitter Sentiment Analysis
Alleviating Data Sparsity for Twitter Sentiment Analysis
Knowledge Media Institute - The Open University
Journal club presentation
DIE 20130724
DIE 20130724
Tokyo Tech
This presentation is for swdm'15. https://sites.google.com/site/swdmwww15/program
Swdm15
Swdm15
BabaSeigo
Tsai, Chun-Hua, and Peter Brusilovsky. 2019. "Evaluating Visual Explanations for Similarity-Based Recommendations: User Perception and Performance." In the 27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2019, 22-30. Larnaca, Cyprus: ACM.
UMAP 2019 talk Evaluating Visual Explanations for Similarity-Based Recommenda...
UMAP 2019 talk Evaluating Visual Explanations for Similarity-Based Recommenda...
Peter Brusilovsky
Prepared as an assignment for CS410: Text Information Systems in Spring 2016
Tutorial on Relationship Mining In Online Social Networks
Tutorial on Relationship Mining In Online Social Networks
pjing2
Poster presented at WiNLP workshop at ACL 2017 in Vancouver. Describes approaches to recognizing humorous tweets based on language models and LSTM.
Who's to say what's funny? A computer using Language Models and Deep Learning...
Who's to say what's funny? A computer using Language Models and Deep Learning...
University of Minnesota, Duluth
MSR 2011 Talk slides
MSR presentation
MSR presentation
Shivani Rao
Learning to rankの評価手法
Learning to rankの評価手法
Kensuke Mitsuzawa
Recommandé
Twitter has brought much attention recently as a hot research topic in the domain of sentiment analysis. Training sentiment classifiers from tweets data often faces the data sparsity problem partly due to the large variety of short and irregular forms introduced to tweets because of the 140-character limit. In this work we propose using two different sets of features to alleviate the data sparseness problem. One is the semantic feature set where we extract semantically hidden concepts from tweets and then incorporate them into classifier training through interpolation. Another is the sentiment-topic feature set where we extract latent topics and the associated topic sentiment from tweets, then augment the original feature space with these sentiment-topics. Experimental results on the Stanford Twitter Sentiment Dataset show that both feature sets outperform the baseline model using unigrams only. Moreover, using semantic features rivals the previously reported best result. Using sentiment-topic features achieves 86.3% sentiment classification accuracy, which outperforms existing approaches.
Alleviating Data Sparsity for Twitter Sentiment Analysis
Alleviating Data Sparsity for Twitter Sentiment Analysis
Knowledge Media Institute - The Open University
Journal club presentation
DIE 20130724
DIE 20130724
Tokyo Tech
This presentation is for swdm'15. https://sites.google.com/site/swdmwww15/program
Swdm15
Swdm15
BabaSeigo
Tsai, Chun-Hua, and Peter Brusilovsky. 2019. "Evaluating Visual Explanations for Similarity-Based Recommendations: User Perception and Performance." In the 27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2019, 22-30. Larnaca, Cyprus: ACM.
UMAP 2019 talk Evaluating Visual Explanations for Similarity-Based Recommenda...
UMAP 2019 talk Evaluating Visual Explanations for Similarity-Based Recommenda...
Peter Brusilovsky
Prepared as an assignment for CS410: Text Information Systems in Spring 2016
Tutorial on Relationship Mining In Online Social Networks
Tutorial on Relationship Mining In Online Social Networks
pjing2
Poster presented at WiNLP workshop at ACL 2017 in Vancouver. Describes approaches to recognizing humorous tweets based on language models and LSTM.
Who's to say what's funny? A computer using Language Models and Deep Learning...
Who's to say what's funny? A computer using Language Models and Deep Learning...
University of Minnesota, Duluth
MSR 2011 Talk slides
MSR presentation
MSR presentation
Shivani Rao
Learning to rankの評価手法
Learning to rankの評価手法
Kensuke Mitsuzawa
Course: Information Retrieval and Extraction Spring'16 IIIT Hyderabad
Semantic Annotation of Documents
Semantic Annotation of Documents
subash chandra
Phd thesis co-advised by Ferran Marqués (UPC) and Shih-Fu Chang (Columbia University). More details in: https://imatge.upc.edu/web/publications/part-based-object-retrieval-binary-partition-trees
Part-based Object Retrieval with Binary Partition Trees
Part-based Object Retrieval with Binary Partition Trees
Universitat Politècnica de Catalunya
Calculating precision
Calculating precision
lindabeekeeper
* Insight of different Evaluation metrics of machine learning algorithm
Data Analysis: Evaluation Metrics for Supervised Learning Models of Machine L...
Data Analysis: Evaluation Metrics for Supervised Learning Models of Machine L...
Md. Main Uddin Rony
This presentation show method to build Decision Tree model with numerical attributes
Building Decision Tree model with numerical attributes
Building Decision Tree model with numerical attributes
Big Data Engineering, Faculty of Engineering, Dhurakij Pundit University
This slide shows classifier evaluation metrics such as Confusion matrix, Precision, Recall, F-Measure, Accuracy, ROC graph and AUC (Area Under Curve).
Evaluation metrics: Precision, Recall, F-Measure, ROC
Evaluation metrics: Precision, Recall, F-Measure, ROC
Big Data Engineering, Faculty of Engineering, Dhurakij Pundit University
Small presentation made for the academic requirement in Mphil CS @ IIITMK
INTRODUCTION INFORMATION RETRIEVAL EVALUVATION
INTRODUCTION INFORMATION RETRIEVAL EVALUVATION
Premsankar Chakkingal
The slides from the Learning to Rank for Recommender Systems tutorial given at ACM RecSys 2013 in Hong Kong by Alexandros Karatzoglou, Linas Baltrunas and Yue Shi.
Learning to Rank for Recommender Systems - ACM RecSys 2013 tutorial
Learning to Rank for Recommender Systems - ACM RecSys 2013 tutorial
Alexandros Karatzoglou
Talk on Search and Machine Learning at Quora that I gave at Salesforce
Machine Learning at Quora (2/26/2016)
Machine Learning at Quora (2/26/2016)
Nikhil Dandekar
Description of a visualization project for visualizing your twitter social graph.
Information Visualization Project
Information Visualization Project
Alexander Nwala
H2O World 2015 - Xavier Amatriain, VP of Engineering @ Quora
H2O World - Quora: Machine Learning Algorithms to Grow the World's Knowledge ...
H2O World - Quora: Machine Learning Algorithms to Grow the World's Knowledge ...
Sri Ambati
Towards trust-aware recommender systems
Towards trust-aware recommender systems
Alberto Lumbreras
Basic steps for inductive and deductive qualitative data analysis in program evaluation.
Who's Afraid of Qualitative Analysis?
Who's Afraid of Qualitative Analysis?
BrigitteScott
Presentation for reading group 30/09/2015, Check out my recent work http://parklize.github.io/#research on User Modeling which is motivated by this presentation.
Analyzing User Modeling on Twitter for Personalized News Recommendations
Analyzing User Modeling on Twitter for Personalized News Recommendations
GUANGYUAN PIAO
A two step ranking solution to the RecSys 2014 Challenge presented at the RecSys 2014 conference on Oct 10, in Foster City (CA, USA) by Behnoush Abdollahi
A Two Step Ranking Solution for Twitter User Engagement