'Sentiment analysis' presentation slideshows

RECENT READING Tom Peters/11 July 2013

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Modeling Opinions and Beyond in Social Media

KDD-2012 Summer School, August 10, 2012, Beijing , China. Modeling Opinions and Beyond in Social Media. Bing Liu University Of Illinois at Chicago liub@cs.uic.edu. Introduction. Why are opinions so important? Opinions are key influencers of our behaviors.

1.15k views • 92 slides

Opinion mining, sentiment analysis, and beyond

Opinion mining, sentiment analysis, and beyond. Bettina Berendt Department o f Computer Science KU Leuven, Belgium http://people.cs.kuleuven.be/~bettina.berendt / Summer School Foundations and Applications of Social Network Analysis & Mining , June 2-6, 2014, Athens, Greece.

1.45k views • 79 slides

Opinion Mining and Sentiment Analysis

Opinion Mining and Sentiment Analysis. Slides from Bing Liu and Ronan Feldman. Introduction. Two main types of textual information. Facts and Opinions Note: factual statements can imply opinions too.

1.03k views • 76 slides

Text Mining

918 views • 63 slides

LINGUISTICA GENERALE E COMPUTAZIONALE

LINGUISTICA GENERALE E COMPUTAZIONALE. SENTIMENT ANALYSIS. FACTS AND OPINIONS. Two main types of textual information on the Web: FACTS and OPINIONS Current search engines search for facts (assume they are true) Facts can be expressed with topic keywords .

789 views • 62 slides

807 - TEXT ANALYTICS

807 - TEXT ANALYTICS. Massimo Poesio Lecture 4: Sentiment analysis (aka Opinion Mining). FACTS AND OPINIONS. Two main types of textual information on the Web: FACTS and OPINIONS Current search engines search for facts (assume they are true) Facts can be expressed with topic keywords .

802 views • 60 slides

Practical Natural Language Processing

808 views • 57 slides

Abbas Alidina @ AbbasAlidina AbbasAlidina.com

Social Media Monitoring. Abbas Alidina @ AbbasAlidina AbbasAlidina.com. Why Measure Social Media?. Exercise: Understanding Customers. Why do customers interact with you on social media?. IBM Social Media Study. The Social Media Perception Gap. Social Monitoring and Social Analytics.

670 views • 46 slides

Semantic Infrastructure Workshop Development

722 views • 43 slides

Text Analytics Evaluation A Case Study: Amdocs

547 views • 42 slides

Demystifying Systems for Interactive and Real-time Analytics

Demystifying Systems for Interactive and Real-time Analytics. The BigFrame Team. Duke University, Hong Kong Polytechnic University, and HP Labs. Analytics System Landscape. Streaming. Dataflow. MapReduce. Graph. Multi-tenant. MPP DB. Array DB. Columnar. Mixed. Text Analytics.

571 views • 42 slides

498 views • 38 slides

Link Prediction and Sentiment Analysis on Amazon products

Link Prediction and Sentiment Analysis on Amazon products. Presenters: Vishal Mishra Ashray Bhandare Subhrajit Majumder. Agenda. Background Analysis Dataset Problem formulation Methodology Simulation Preprocessing Link Prediction Sentiment Analysis Experimental results

317 views • 31 slides

Sentiment Analysis

397 views • 25 slides

Utilizing Social Media to Understand Human Interaction with Extreme Media Events - The Superstorm Sandy Beta Test

Utilizing Social Media to Understand Human Interaction with Extreme Media Events - The Superstorm Sandy Beta Test. Arthur G. Cosby Somya D. Mohanty. National Weather Service Online Webinar Jul 16 , 2013. NASA-NOAA Suomi National Polar-orbiting Partnership (NPP) satellite. Twitter.

319 views • 19 slides

Review

Review. Data Visualization Process. Text Analysis/Text Mining. Derive high-quality information on patterns and trends in the text via statistical pattern learning Word frequency analysis Sentiment analysis Text categorization Text clustering Related fields Computational Linguistics

366 views • 16 slides

Unsupervised Clustering of People, Places & Organizations in U.S. Diplomatic Cables

Unsupervised Clustering of People, Places & Organizations in U.S. Diplomatic Cables. Xuwen Cao Beyang Liu. Process Outline. Identify entities in 3891 leaked U.S. diplomatic cables published by Wikileaks Extract features from window around entities Sentiment scores Co-occurying entities

208 views • 8 slides

Jeff Jenkins (Team Leader) Justin Frey John Gastreich MIS 510 Web Mining Dr. Chen April 28, 2010

259 views • 8 slides

Data + ALgo = Logic Logic + Data = ANSwers

Data + ALgo = Logic Logic + Data = ANSwers. Jan 2014. STAT. ML. Learn from experience a nd generalize. PR. AI. predictions. DM. Most likely classifications. CS. Discovery of unknown patterns. analytics. FOR EXAMPLE. AUTO TRANSMISSIONS ROBOT TRAINING STOCK PICKERS SIRI

169 views • 7 slides