Automatic sentiment classification has been extensively studied and applied in recent years. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is impractical. We investigate domain adaptation for sentiment classifiers, focusing on online reviews for different types of products. First, we extend to sentiment classification the recently-proposed structural correspondence learning (SCL) algorithm, reducing the relative error due to adaptation between domains by an average of 30 % over the original SCL algorithm and 46 % over a supervised baseline. Second, we identify a measure of domain similarity that correlates well with the potential for adaptation of a classifier from one domain to another. This measure could for instance be used to select a small set of domains to annotate whose trained classifiers would transfer well to many other domains. 1

In this paper we investigate a structured model for jointly classifying the sentiment of text at varying levels of granularity. Inference in the model is based on standard sequence classification techniques using constrained Viterbi to ensure consistent solutions. The primary advantage of such a model is that it allows classification decisions from one level in the text to influence decisions at another. Experiments show that this method can significantly reduce classification error relative to models trained in isolation. 1

Textual information in the world can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about entities, events and their properties. Opinions are usually subjective expressions that describe people’s sentiments, appraisals or feelings toward entities, events and their properties. The concept of opinion is very broad. In this chapter, we only focus on opinion expressions that convey people’s positive or negative sentiments. Much of the existing research on textual information processing has been focused on mining and retrieval of factual information, e.g., information retrieval, Web search, text classification, text clustering and many other text mining and natural language processing tasks. Little work had been done on the processing of opinions until only recently. Yet, opinions are so important that whenever we need to make a decision we want to hear others ’ opinions. This is not only true for individuals but also true for organizations. One of the main reasons for the lack of study on opinions is the fact that there was little opinionated text available before the World Wide Web. Before the Web, when an individual needed to make a decision, he/she typically asked for opinions from friends and families. When an organization wanted to find the opinions or sentiments of the general public about its products and services, it conducted opinion polls, surveys, and focus groups. However, with the Web, especially with the explosive growth of the usergenerated

In this paper, we investigate structured models for document-level sentiment classification. When predicting the sentiment of a subjective document (e.g., as positive or negative), it is well known that not all sentences are equally discriminative or informative. But identifying the useful sentences automatically is itself a difficult learning problem. This paper proposes a joint two-level approach for document-level sentiment classification that simultaneously extracts useful (i.e., subjective) sentences and predicts document-level sentiment based on the extracted sentences. Unlike previous joint learning methods for the task, our approach (1) does not rely on gold standard sentence-level subjectivity annotations (which may be expensive to obtain), and (2) optimizes directly for document-level performance. Empirical evaluations on movie reviews and U.S. Congressional floor debates show improved performance over previous approaches. 1

Work on sentiment analysis often focuses on the words and phrases that people use in overtly opinionated text. In this paper, we introduce a new approach to the problem that focuses not on lexical indicators, but on the syntactic “packaging ” of ideas, which is well suited to investigating the identification of implicit sentiment, or perspective. We establish a strong predictive connection between linguistically well motivated features and implicit sentiment, and then show how computational approximations of these features can be used to improve on existing state-of-the-art sentiment classification results. 1

My first thanks must go to Fernando Pereira. He was a wonderful advisor, and every aspect of this thesis has benefitted from his insight. At times I was a difficult, even unruly graduate student, and Fernando had patience with all my ideas, whether good or bad. What I’ll miss most, though, is the quick trip to Fernando’s office, coming away with new insights on everything from numerical underflow to the state of the academic community in machine learning and NLP. In addition to Fernando, this thesis was shaped by a great committee. Having Ben Taskar as committee chairman has given me the perfect excuse to interrupt his workday with new, ostensibly-thesis-related machine learning ideas. Mark Liberman and Mitch Marcus brought a much-needed linguistic perspective to a thesis on language, and many of the techniques described are based on work by Tong Zhang, who kindly served as my external committee member. Although he didn’t directly serve on my committee, Shai Ben-David got me started on the theoretical aspects of this work, and chapter 4 grew out of work I co-authored with him. I was also fortunate to have a great academic family. With brothers (and one sister!)

The increasing availability of digitized text presents enormous opportunities for social scientists. Yet hand coding many blogs, speeches, government records, newspapers, or other sources of unstructured text is infeasible. Although computer scientists have methods for automated content analysis, most are optimized to classify individual documents, whereas social scientists instead want generalizations about the population of documents, such as the proportion in a

To evaluate and extend existing natural language processing techniques into the domain of informal online political discussions. Design/methodology/approach A database of postings from a U.S. political discussion site was collected, along with self-reported political orientation data for the users. A variety of sentiment analysis, text classification, and social network analysis methods were applied to the postings and evaluated against the users ’ self-descriptions. Findings Purely text-based methods performed poorly, but could be improved using techniques which took into account the users ’ position in the online community. Research limitations The techniques we applied here are fairly simple, and more sophisticated learning algorithms may yield better results for text-based classification. Practical implications This work suggests that social network analysis is an important tool for

This work investigates design choices in modeling a discourse scheme for improving opinion polarity classification. For this, two diverse global inference paradigms are used: a supervised collective classification framework and an unsupervised optimization framework. Both approaches perform substantially better than baseline approaches, establishing the efficacy of the methods and the underlying discourse scheme. We also present quantitative and qualitative analyses showing how the improvements are achieved. 1

Many real-world datasets can be clustered alongmultiple dimensions. Forexample, text documentscanbeclusterednotonlybytopic, but also by the author’s gender or sentiment. Unfortunately, traditional clustering algorithms produce only a single clustering of a dataset, effectively providing a user with just a single view of the data. In this paper, we propose a new clustering algorithm that can discover in an unsupervised manner each clustering dimension along which a dataset can be meaningfully clustered. Its ability to revealthe important clustering dimensions of a dataset in an unsupervised manner is particularly appealing for those users who have no idea of how a dataset can possibly be clustered. Wedemonstrateitsviabilityonseveral challenging text classification tasks. 1.