The problem of automatic extraction of sentiment expressions from informal text, as in microblogs such as tweets is a recent area of investigation. Compared to formal text, such as in product reviews or news articles, one of the key challenges lies in the wide diversity and informal nature of sentiment expressions that cannot be trivially enumerated or captured using predefined lexical patterns. In this work, we present an optimization-based approach to automatically extract sentiment expressions for a given target (e.g., movie, or person) from a corpus of unlabeled tweets. Specifically, we make three contributions: (i) we recognize a diverse and richer set of sentiment-bearing expressions in tweets, including formal and slang words/phrases, not limited to pre-specified syntactic patterns; (ii) instead of associating sentiment with an entire tweet, we assess the target-dependent polarity of each sentiment expression. The polarity of sentiment expression is determined by the nature of its target; (iii) we provide a novel formulation of assigning polarity to a sentiment expression as a constrained optimization problem over the tweet corpus. Experiments conducted on two domains, tweets mentioning movie and person entities, show that our approach improves accuracy in comparison with several baseline methods, and that the improvement becomes more prominent with increasing corpus sizes.

The ICWSM 2012 conference tutorials will be How to Analyze Massive Social Network Datasets without a Cluster, presented by Derek Ruths; Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL, presented by Marc Smith; Evidenced-Based Social Design of Online Communities: Getting to Critical Mass and Encouraging Contributions, presented by Paul Resnick and Robert Kraut; Sentiment Mining from User Generated Content, presented by Lyle Ungar and Ronen Feldman; and Information Extraction for Social Media Anaylsis, presented by Denilson Barbosa.

Speech acts provide good insights into the communicative behavior of tweeters on Twitter. This paper is mainly concerned with speech act recognition in Twitter as a multi-class classification problem, for which we propose a set of word-based and character-based features. Inexpensive, robust and efficient, our method achieves an average F1 score of nearly 0.7 with the existence of much noise in our annotated Twitter data. In view of the deficiency of training data for the task, we experimented extensively with different configurations of training and test data, leading to empirical findings that may provide valuable reference for building benchmark datasets for sustained research on speech act recognition in Twitter.

This paper focuses on performing Sentiment Analysis of Twitter by adapting data from other domains, commonly referred to as Domain Adaptation. While we show that Domain Adaptation is useful in predicting sentiments, we propose different techniques to select an out-of-domain data source that would aid in Sentiment Analysis. Additionally, we suggest two iterative algorithms based on Expectation-Maximization (EM) and Rocchio SVM that filter noisy data during adaptation and train only on valid data. Finally, we explore a couple of metrics, Mutual Information and Cosine distance to measure similarity between different domains of data. We use Twitter and Blippr as data sources and perform binary sentiment (positive and negative sentiments) classification.

The Stock Sonar (TSS) is a stock sentiment analysis application based on a novel hybrid approach. While previous work focused on document level sentiment classification, or extracted only generic sentiment at the phrase level, TSS integrates sentiment dictionaries, phrase-level compositional patterns, and predicate-level semantic events. TSS generates precise in text sentiment tagging as well as sentiment-oriented event summaries for a given stock, which are also aggregated into sentiment scores. Hence, TSS allows investors to get the essence of thousands of articles every day and may help them to make timely, informed trading decisions. The extracted sentiment is also shown to improve the accuracy of an existing document-level sentiment classifier.

Sentiment analysis is the task of determining the attitude (positive or negative) of documents. While the polarity of words in the documents is informative for this task, polarity of some words cannot be determined without domain knowledge. Detecting word polarity thus poses a challenge for multiple-domain sentiment analysis. Previous approaches tackle this problem with transfer learning techniques, but they cannot handle multiple source domains and multiple target domains. This paper proposes a novel Bayesian probabilistic model to handle multiple source and multiple target domains. In this model, each word is associated with three factors: Domain label, domain dependence/independence and word polarity. We derive an efficient algorithm using Gibbs sampling for inferring the parameters of the model, from both labeled and unlabeled texts. Using real data, we demonstrate the effectiveness of our model in a document polarity classification task compared with a method not considering the differences between domains. Moreover our method can also tell whether each word's polarity is domain-dependent or domain-independent. This feature allows us to construct a word polarity dictionary for each domain.

Much of opinion mining research focuses on product reviews because reviews are opinion-rich and contain little irrelevant information. However, this cannot be said about online discussions and comments. In such postings, the discussions can get highly emotional and heated with many emotional statements, and even personal attacks. As a result, many of the postings and sentences do not express positive or negative opinions about the topic being discussed. To find people’s opinions on a topic and its different aspects, which we call evaluative opinions, those irrelevant sentences should be removed. The goal of this research is thus to identify evaluative opinion sentences. A novel unsupervised approach is proposed to solve the problem, and our experimental results show that it performs well.

In the past few years, sentiment analysis and opinion mining becomes a popular and important task. These studies all assume that their opinion resources are real and trustful. However, they may encounter the faked opinion or opinion spam problem. In this paper, we study this issue in the context of our product review mining system. On product review site, people may write faked reviews, called review spam, to promote their products, or defame their competitors' products. It is important to identify and filter out the review spam. Previous work only focuses on some heuristic rules, such as helpfulness voting, or rating deviation, which limits the performance of this task. In this paper, we exploit machine learning methods to identify review spam. Toward the end, we manually build a spam collection from our crawled reviews. We first analyze the effect of various features in spam identification. We also observe that the review spammer consistently writes spam. This provides us another view to identify review spam: we can identify if the author of the review is spammer. Based on this observation, we provide a two-view semi-supervised method, co-training, to exploit the large amount of unlabeled data. The experiment results show that our proposed method is effective. Our designed machine learning methods achieve significant improvements in comparison to the heuristic baselines.

Trained on the imbalanced labeled data, most classification Various semi-supervised learning methods have algorithms tend to predict test samples as the majority class been proposed recently to solve the longstanding and may ignore the minority class. Although many methods, shortage problem of manually labeled data in sentiment such as re-sampling [Chawla et al., 2002], one-class classification classification. However, most existing studies [Juszczak and Duin, 2003], and cost-sensitive assume the balance between negative and positive learning [Zhou and Liu, 2006], have been proposed to solve samples in both the labeled and unlabeled data, this issue, it is still unclear as to which method is more which may not be true in reality. In this paper, we suitable to handle the imbalanced problem in sentiment investigate a more common case of semi-supervised classification and whether the method is extendable to learning for imbalanced sentiment classification.

We call this task the rating-inference task; Traditional sentiment analysis mainly considers It determines an author's polarity evaluation within a multipoint binary classifications of reviews, but in many scale (e.g. one to five "stars"). We explore solutions for real-world sentiment classification problems, nonbinary this task in the context of product or service reviews, which review ratings are more useful. This is especially are one of the most important opinion resources and widely true when consumers wish to compare two used by costumers and companies. We observe that in many products, both of which are not negative. Previous real-world scenarios, it is important to provide numerical ratings work has addressed this problem by extracting rather than binary decisions, especially when a customer various features from the review text for learning a compares several candidate products, all of them are positive predictor. Since the same word may have different in a binary classification, to make a purchase decision, since sentiment effects when used by different reviewers customers not only need to know whether a product is good or on different products, we argue that it is necessary not, but also how good the product is. A recent study pointed to model such reviewer and product dependent effects out that many consumers are willing to pay at least 20% percent in order to predict review ratings more accurately.