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...ed sentiment and the manually collected poll ratings. Sentiment classification has been studied for years on various text corpus, like newspaper articles [33], movie reviews [31], and product reviews =-=[5, 11, 23]-=-. The basic idea of the methods is to build a sophisticated feature space, which can effectively represent the sentiment status of the texts. Existing methods, which are designed for traditional i.i.d...

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...ng a number of hand-crafted features. Unlike the previous studies, we focus on learning discriminative features automatically from massive distant-supervised tweets. 1556 2.2 Learning Continuous Representations for Sentiment Classification Pang et al. (2002) pioneer this field by using bagof-word representation, representing each word as a one-hot vector. It has the same length as the size of the vocabulary, and only one dimension is 1, with all others being 0. Under this assumption, many feature learning algorithms are proposed to obtain better classification performance (Pang and Lee, 2008; Liu, 2012; Feldman, 2013). However, the one-hot word representation cannot sufficiently capture the complex linguistic characteristics of words. With the revival of interest in deep learning (Bengio et al., 2013), incorporating the continuous representation of a word as features has been proving effective in a variety of NLP tasks, such as parsing (Socher et al., 2013a), language modeling (Bengio et al., 2003; Mnih and Hinton, 2009) and NER (Turian et al., 2010). In the field of sentiment analysis, Bespalov et al. (2011; 2012) initialize the word embedding by Latent Semantic Analysis and further repres...

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..., Beijing, China. Copyright 2012 ACM 978-1-4503-1462-6/12/08...$15.00. . Although there is a large body of literature on social media analysis such as social network analysis [14], sentiment analysis =-=[27, 34]-=-, and grouping people into different camps [1, 32, 40, 42], to the best of our knowledge, limited research has been done on the fine-grained analysis of discussion/debate forums as proposed in this pa...

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...or download and use. Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media 216 2. Background and Related Work Sentiment analysis, or opinion mining, is an active area of study in the field of natural language processing that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions via the computational treatment of subjectivity in text. It is not our intention to review the entire body of literature concerning sentiment analysis. Indeed, such an endeavor would not be possible within the limited space available (such treatments are available in Liu (2012) and Pang & Lee (2008)). We do provide a brief overview of canonical works and techniques relevant to our study. 2.1 Sentiment Lexicons A substantial number of sentiment analysis approaches rely greatly on an underlying sentiment (or opinion) lexicon. A sentiment lexicon is a list of lexical features (e.g., words) which are generally labeled according to their semantic orientation as either positive or negative (Liu, 2010). Manually creating and validating such lists of opinion-bearing features, while being among the most robust methods for generating reliable sentiment lexicons, is also one o...

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...e.g., The Guardian, 2012). This social science use of sentiment analysis is different from its most common use for detecting the polarity of opinions of consumer products or movies (Pang & Lee, 2008; =-=Liu, 2012-=-) although it can exploit similar methods. An important problem with many types of sentiment analysis is that it can be highly topic dependent in the sense that algorithms that predict sentiment accur...