## 2 Related Work  
While much work has recently focused on the analysis of social media in order to get a feel for what people think about current topics of interest, there are, however, still many challenges to be faced. State-of-the-art opinion mining approaches that focus on product reviews are not necessarily suitable for our task, partly because they typically operate within a single narrow domain, and partly because the target of the opinion is either known in advance or at least has a limited subset (e.g., film titles, product names, companies, political parties, _etc._). Furthermore, quality is often still not that good: a recent survey of 9 state-of-the-art tools tested on a set of 30,000 short texts found that the average accuracy was only 50% [3]. In general, sentiment detection techniques can be roughly divided into lexicon-based methods [4] and machine-learning methods, e.g., [5]. Lexicon-based methods rely on a sentiment lexicon, a collection of known and pre-compiled sentiment terms. Machine learning approaches make use of syntactic and/or linguistic features, and hybrid approaches are very common, with sentiment lexicons playing a key role in the majority of methods. For example, [PERSON] _et al._[6] establish the polarity of reviews by identifying the polarity of the adjectives that appear in them, with a reported accuracy of about 10% higher than pure machine learning techniques. However, such relatively successful techniques often fail when moved to new domains or text types, because they are inflexible regarding the ambiguity of sentiment terms. The context in which a term is used can change its meaning, particularly for adjectives in sentiment lexicons [7]. Several evaluations have shown the usefulness of contextual information [8], and have identified context words with a high impact on the polarity of ambiguous terms [9]. A further bottleneck is the time-consuming creation of these sentiment dictionaries, though solutions have been proposed in the form of crowdsourcing techniques [10]. Almost all the work on opinion mining from Twitter has used machine learning techniques. [PERSON] and [PERSON] [11] aimed to classify arbitrary tweets on the basis of positive, negative and neutral sentiment, constructing a simple binary classifier which used n-gram and POS features, and trained on instances which had been annotated according to the existence of positive and negative emoticons. Their approach has much in common with an earlier sentiment classifier constructed by [PERSON] _et al._[12], which also used unigrams, bigrams and POS tags, though the former demonstrated through analysis that the distribution of certain POS tags varies between positive and negative posts. One of the reasons for the relative paucity of linguistic techniques for opinion mining on social media is most likely due to the difficulties in using NLP on low quality text [13]; for example, the Stanford NER drops from 90.8% F1 to 45.88% when applied to a corpus of tweets [14]. More recent work, however, has attempted to overcome this problem: see e.g., [15]. Our work includes the detection and analysis of sarcasm, which is a particular problem for opinion mining in social media, which no systems to our knowledge have addressed satisfactorily, although there have been a number of recent works attempting to detect sarcasm in tweets and other user-generated content, e.g., [16; 17; 18; 19]. It appears that none of these approaches go beyond this step of sarcasm detection: even when a statement is known to be sarcastic, one cannot necessarily predict how this will affect the sentiment expressed. In this paper, we do not describe in detail our work on sarcasm detection, but details can be found in [20]. Our approach to opinion mining takes inspiration from a number of sources. The opinion extraction and classification is most similar to the work of [4] in terms of technique, but because we focus on social media, we need to employ some different strategies to deal with the linguistic issues imposed.