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

How to Do Things With Words

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

Unsupervised vector-based approaches to semantics can model rich lexical meanings, but they largely fail to capture sentiment information that is central to many word meanings and important for a wide range of NLP tasks. We present a model that uses a mix of unsupervised and supervised techniques to learn word vectors capturing semantic term--document information as well as rich sentiment content. The proposed model can leverage both continuous and multi-dimensional sentiment information as well as non-sentiment annotations. We instantiate the model to utilize the document-level sentiment polarity annotations present in many online documents (e.g. star ratings). We evaluate the model using small, widely used sentiment and subjectivity corpora and find it out-performs several previously introduced methods for sentiment classification. We also introduce a large dataset of movie reviews to serve as a more robust benchmark for work in this area.

/pdf/learning-word-vectors-for-sentiment-analysis-19n5e5xfor.pdf

Learning Word Vectors for Sentiment Analysis

Sentiment analysis algorithms and applications: A survey

In this paper, we propose a learning approach to train conditional random fields from unaligned data for natural language understanding where input to model learning are sentences paired with predicate formulae (or abstract semantic annotations) without word-level annotations. The learning approach resembles the expectation maximization algorithm. It has two advantages, one is that only abstract annotations are needed instead of fully word-level annotations, and the other is that the proposed learning framework can be easily extended for training other discriminative models, such as support vector machines, from abstract annotations. The proposed approach has been tested on the DARPA Communicator Data. Experimental results show that it outperforms the hidden vector state (HVS) model, a modified hidden Markov model also trained on abstract annotations. Furthermore, the proposed method has been compared with two other approaches, one is the hybrid framework (HF) combining the HVS model and the support vector hidden Markov model, and the other is discriminative training of the HVS model (DT). The proposed approach gives a relative error reduction rate of 18.7% and 8.3% in F-measure when compared with HF and DT respectively.

Learning Conditional Random Fields from Unaligned Data for Natural Language Understanding

Recent progress in leveraging deep learning methods for question answering

With the development of social media tools such as Facebook and Twitter, mainstream media organizations including newspapers and TV media have played an active role in engaging with their audience and strengthening their influence on the recently emerged platforms. In this paper, we analyze the behavior of mainstream media on Twitter and study how they exert their influence to shape public opinion during the UK's 2010 General Election. We first propose an empirical measure to quantify mainstream media bias based on sentiment analysis and show that it correlates better with the actual political bias in the UK media than the pure quantitative measures based on media coverage of various political parties. We then compare the information diffusion patterns from different categories of sources. We found that while mainstream media is good at seeding prominent information cascades, its role in shaping public opinion is being challenged by journalists since tweets from them are more likely to be retweeted and they spread faster and have longer lifespan compared to tweets from mainstream media. Moreover, the political bias of the journalists is a good indicator of the actual election results.

http://www.hlt.utdallas.edu/~zywei/paper/1-wei-hypertext2013.pdf

Mainstream media behavior analysis on Twitter: a case study on UK general election

Storyline extraction from news streams aims to extract events under a certain news topic and reveal how those events evolve over time. It requires algorithms capable of accurately extracting events from news articles published in different time periods and linking these extracted events into coherent stories. The two tasks are often solved separately, which might suffer from the problem of error propagation. Existing unified approaches often consider events as topics, ignoring their structured representations. In this paper, we propose a non-parametric generative model to extract structured representations and evolution patterns of storylines simultaneously. In the model, each storyline is modelled as a joint distribution over some locations, organizations, persons, keywords and a set of topics. We further combine this model with the Chinese restaurant process so that the number of storylines can be determined automatically without human intervention. Moreover, per-token Metropolis-Hastings sampler based on light latent Dirichlet allocation is employed to reduce sampling complexity. The proposed model has been evaluated on three news corpora and the experimental results show that it outperforms several baseline approaches.

Unsupervised storyline extraction from news articles

In most previous studies, the aspect-related text is considered an important clue for the Aspect-based Sentiment Analysis (ABSA) task, and thus various attention mechanisms have been proposed to leverage the interactions between aspects and context. However, it is observed that some sentiment expressions carry the same polarity regardless of the aspects they are associated with. In such cases, it is not necessary to incorporate aspect information for ABSA. More observations on the experimental results show that blindly leveraging interactions between aspects and context as features may introduce noises when analyzing those aspect-invariant sentiment expressions, especially when the aspect-related annotated data is insufficient. Hence, in this paper, we propose an Adversarial Multi-task Learning framework to identify the aspect-invariant/dependent sentiment expressions without extra annotations. In addition, we adopt a gating mechanism to control the contribution of representations derived from aspect-invariant and aspect-dependent hidden states when generating the final contextual sentiment representations for the given aspect. This essentially allows the exploitation of aspect-invariant sentiment features for better ABSA results. Experimental results on two benchmark datasets show that extending existing neural models using our proposed framework achieves superior performance. In addition, the aspect-invariant data extracted by the proposed framework can be considered as pivot features for better transfer learning of the ABSA models on unseen aspects.

Yulan He

Papers

Learning Conditional Random Fields from Unaligned Data for Natural Language Understanding

Recent progress in leveraging deep learning methods for question answering

Mainstream media behavior analysis on Twitter: a case study on UK general election

Unsupervised storyline extraction from news articles

Aspect-invariant Sentiment Features Learning: Adversarial Multi-task Learning for Aspect-based Sentiment Analysis