Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

/pdf/sentiment-analysis-and-opinion-mining-8314pykwrf.pdf

Sentiment Analysis and Opinion Mining

Sentiment analysis or opinion mining is the computational study of people’s opinions, appraisals, attitudes, and emotions toward entities, individuals, issues, events, topics and their attributes. The task is technically challenging and practically very useful. For example, businesses always want to find public or consumer opinions about their products and services. Potential customers also want to know the opinions of existing users before they use a service or purchase a product.

/pdf/a-survey-of-opinion-mining-and-sentiment-analysis-3ypfl5jtav.pdf

A Survey of Opinion Mining and Sentiment Analysis

The main applications and challenges of one of the hottest research areas in computer science.

Techniques and applications for sentiment analysis

Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results. Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in recent years. This paper first gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis.

Deep learning for sentiment analysis: A survey

Sentiment Analysis and Opinion Mining.

With the booming of microblogs on the Web, people have begun to express their opinions on a wide variety of topics on Twitter and other similar services. Sentiment analysis on entities (e.g., products, organizations, people, etc.) in tweets (posts on Twitter) thus becomes a rapid and effective way of gauging public opinion for business marketing or social studies. However, Twitter's unique characteristics give rise to new problems for current sentiment analysis methods, which originally focused on large opinionated corpora such as product reviews. In this paper, we propose a new entity-level sentiment analysis method for Twitter. The method first adopts a lexiconbased approach to perform entity-level sentiment analysis. This method can give high precision, but low recall. To improve recall, additional tweets that are likely to be opinionated are identified automatically by exploiting the information in the result of the lexicon-based method. A classifier is then trained to assign polarities to the entities in the newly identified tweets. Instead of being labeled manually, the training examples are given by the lexicon-based approach. Experimental results show that the proposed method dramatically improves the recall and the F-score, and outperforms the state-of-the-art baselines. External Posting Date: June 21, 2011 [Fulltext] Approved for External Publication Internal Posting Date: June 21, 2011 [Fulltext]  Copyright 2011 Hewlett-Packard Development Company, L.P. Combining Lexicon-based and Learning-based Methods for Twitter Sentiment Analysis Lei Zhang, Riddhiman Ghosh, Mohamed Dekhil, Meichun Hsu, Bing Liu Hewlett-Packard Laboratories University of Illinois at Chicago 1501 Page Mill Rd., Palo Alto, CA 851 S. Morgan St., Chicago, IL {riddhiman.ghosh, mohamed.dekhil, {lzhang3, liub}@cs.uic.edu meichun.hsu}@hp.com

https://www.hpl.hp.com/techreports/2011/HPL-2011-89.pdf

Lei Zhang

Papers

A Survey of Opinion Mining and Sentiment Analysis

Deep learning for sentiment analysis: A survey

Sentiment Analysis and Opinion Mining.

Combining lexicon-based and learning-based methods for twitter sentiment analysis

Deep Learning for Sentiment Analysis : A Survey