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

The lack of Chinese sentiment corpora limits the research progress on Chinese sentiment classification. However, there are many freely available English sentiment corpora on the Web. This paper focuses on the problem of cross-lingual sentiment classification, which leverages an available English corpus for Chinese sentiment classification by using the English corpus as training data. Machine translation services are used for eliminating the language gap between the training set and test set, and English features and Chinese features are considered as two independent views of the classification problem. We propose a cotraining approach to making use of unlabeled Chinese data. Experimental results show the effectiveness of the proposed approach, which can outperform the standard inductive classifiers and the transductive classifiers. 1

We explore the adaptation of English resources and techniques for text sentiment analysis to a new language, Spanish. Our main focus is the modification of an existing English semantic orientation calculator and the building of dictionaries; however we also compare alternate approaches, including machine translation and Support Vector Machine classification. The results indicate that, although languageindependent methods provide a decent baseline performance, there is also a significant cost to automation, and thus the best path to long-term improvement is through the inclusion of language-specific knowledge and resources. 1.

While traditional work on text clustering has largely focused on grouping documents by topic, it is conceivable that a user may want to cluster documents along other dimensions, such as the author’s mood, gender, age, or sentiment. Without knowing the user’s intention, a clustering algorithm will only group documents along the most prominent dimension, which may not be the one the user desires. To address this problem, we propose a novel way of incorporating user feedback into a clustering algorithm, which allows a user to easily specify the dimension along which she wants the data points to be clustered via inspecting only a small number of words. This distinguishes our method from existing ones, which typically require a large amount of effort on the part of humans in the form of document annotation or interactive construction of the feature space. We demonstrate the viability of our method on several challenging sentiment datasets.

The goal of this work is to produce a classifier that can distinguish subjective sentences from objective sentences for the Urdu language. The amount of labeled data required for training automatic classifiers can be highly imbalanced especially in the multilingual paradigm as generating annotations is an expensive task. In this work, we propose a cotraining approach for subjectivity analysis in the Urdu language that augments the positive set (subjective set) and generates a negative set (objective set) devoid of all samples close to the positive ones. Using the data set thus generated for training, we conduct experiments based on SVM and VSM algorithms, and show that our modified VSM based approach works remarkably well as a sentence level subjectivity classifier. 1

The lack of reliable Chinese sentiment resources limits research progress on Chinese sentiment classification. However, there are many freely available English sentiment resources on the Web. This article focuses on the problem of cross-lingual sentiment classification, which leverages only available English resources for Chinese sentiment classification. We first investigate several basic methods (including lexicon-based methods and corpus-based methods) for cross-lingual sentiment classification by simply leveraging machine translation services to eliminate the language gap, and then propose a bilingual co-training approach to make use of both the English view and the Chinese view based on additional unlabeled Chinese data. Experimental results on two test sets show the effectiveness of the proposed approach, which can outperform basic methods and transductive methods. 1.

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In this paper, we study the problem of using an annotated corpus in English for the same natural language processing task in another language. While various machine translation systems are available, automated translation is still far from perfect. To minimize the noise introduced by translations, we propose to use only key ‘reliable ” parts from the translations and apply structural correspondence learning (SCL) to find a low dimensional representation shared by the two languages. We perform experiments on an English-Chinese sentiment classification task and compare our results with a previous cotraining approach. To alleviate the problem of data sparseness, we create extra pseudo-examples for SCL by making queries to a search engine. Experiments on real-world on-line review data demonstrate the two techniques can effectively improve the performance compared to previous work. 1

This paper presents two instance-level transfer learning based algorithms for cross lingual opinion analysis by transferring useful translated opinion examples from other languages as the supplementary training data for improving the opinion classifier in target language. Starting from the union of small training data in target language and large translated examples in other languages, the Transfer AdaBoost algorithm is applied to iteratively reduce the influence of low quality translated examples. Alternatively, starting only from the training data in target language, the Transfer Self-training algorithm is designed to iteratively select high quality translated examples to enrich the training data set. These two algorithms are applied to sentence- and document-level cross lingual opinion analysis tasks, respectively. The evaluations show that these algorithms effectively improve the opinion analysis by exploiting small target language training data and large cross lingual training data. 1

Subjectivity and sentiment analysis focuses on the automatic identification of private states, such as opinions, emotions, sentiments, evaluations, beliefs, and speculations in natural language. While subjectivity classification labels text as either subjective or objective, sentiment classification adds an additional level of granularity, by further classifying subjective text as either positive, negative