Existing work in fine-grained sentiment analysis focuses on sentences and phrases but ignores the contribution of individual words and their grammatical connections . This is because of a lack of both ( 1 ) annotated data at the word level and ( 2 ) algorithms that can leverage syntactic information in a principled way . We address the first need by annotating articles from the information technology business press via crowdsourcing to provide training and testing data . To address the second need , we propose a suffix-tree data structure to represent syntactic relationships between opinion targets and words in a sentence that are opinion-bearing . We show that a factor graph derived from this data structure acquires these relationships with a small number of word-level features . We demonstrate that our supervised model performs better than baselines that ignore syntactic features and constraints . The terms " sentiment analysis " and " opinion mining " cover a wide body of research on and development of systems that can automatically infer emotional states from text ( after Pang and Lee ( 2008 ) we use the two names interchangeably ) . Sentiment analysis plays a large role in business , politics , and is itself a vibrant research area ( Bollen et al . , 2010 ) . Effective sentiment analysis for texts such as newswire depends on the ability to extract who ( source ) is saying what ( target ) . Fine-grained sentiment analysis requires identifying the sources and targets directly relevant to sentiment bearing expressions ( Ruppenhofer et al . , 2008 ) . For example , consider the following sentence from a major information technology ( IT ) business journal : Lloyd Hession , chief security officer at BT Radianz in New York , said that virtualization also opens up a slew of potential network access control issues . There are three entities in the sentence that have the capacity to express an opinion : Lloyd Hession , BT Radianz , and New York . These are potential opinion sources . There are also a number of mentioned concepts that could serve as the topic of an opinion in the sentence , or target . These include all the sources , but also " virtualization " , " network access control " , " network " , and so on . The challenging task is to discriminate between these mentions and choose the ones that are relevant to the user . Furthermore , such a system must also indicate the content of the opinion itself . This means that we are actually searching for all triples { source , target , opinion } in this sentence ( Kim and Hovy , 2006 ) and throughout each document in the corpus . In this case , we want to identify that Lloyd Hession is the source of an opinion , " slew of network issues , " about a target , virtualization . Providing such fine-grained annotations would enrich information extraction , question answering , and corpus exploration applications by letting users see who is saying what with what opinion ( Wilson et al . , 2005 ; Stoyanov and Cardie , 2006 ) . We motivate the need for a grammatically-focused approach to fine-grained opinion mining and situate it within the context of existing work in Section 2 . We propose a supervised technique for learning opiniontarget relations from dependency graphs in a way that preserves syntactic coherence and semantic compositionality . In addition to being theoretically sound -a lacuna identified in many sentiment systems1 -such approaches improve downstream sentiment tasks ( Moilanen and Pulman , 2007 ) . There are multiple types of downstream tasks that potentially require the retrieval of { source , target , opinion } relations on a sentence-by-sentence basis . An increasingly significant application area is in the use of large corpora in social science . This area of research requires the exploration and aggregation of data about the relationships between discourses , organizations , and people . For example , the IT business press data that we use in this work belongs to a larger research program ( Tsui et al . , 2009 ; Sayeed et al . , 2010 ) of exploring industry opinion leadership . IT business press text is one type of text in which many entities and opinions can appear intermingled with one another in a small amount of text . Another application for fine-grained sentiment relation retrieval of this type is paraphrasing , where attribution of which opinion belongs to which entities may be important for producing useful and accurate output , since source and target identification errors can change the entire meaning of an output text . Unlike previous approaches that ignore syntax , we use a sentence 's syntactic structure to build a probabilistic model that encodes whether a word is opinion bearing as a latent variable . We build a data structure we call a " syntactic relatedness trie " ( Section 3 ) that serves as the skeleton for a graphical model over the sentiment relevance of words ( Section 4 ) . This approach allows us to learn features that predict opinion bearing constructions from grammatical structures . Because of a dearth of resources for this fine-grained task , we also develop new crowdsourcing techniques for labeling word-level , syntactically informed sen-timent ( Section 5 ) . We use inference techniques to uncover grammatical patterns that connect opinionexpressing words and target entities ( Section 6 ) performing better than using syntactically uninformed methods . In this work , we have applied machine learning to produce a robust modeling of syntactic structure for an information extraction application . A solution to the problem of modeling these structures requires the development of new techniques that model complex linguistic relationships in an application-dependent way . We have shown that we can mine these relationships without being overcome by the data-sparsity issues that typically stymie learning over complex linguistic structure . The limitations on these techniques ultimately find their root in the difficulty in modeling complex syntactic structures that simultaneously exclude irrelevant portions of the structure while maintaining connected relations . Our technique uses a structurelabelling scheme that enforces connectedness . Enforcing connected structure is not only necessary to produce useful results but also to improve accuracy . Further performance gains might be possible by enriching the feature set . For example , the POS tagset used by the Stanford parser contains multiple verb tags that represent different English tenses and numbers . For the purpose of sentiment relations , it is possible that the differences between verb tags are too small to matter and are causing data sparsity issues . Thus , we could additional features that " back off " to general verb tags .

Challenge: Existing methods for fine-grained sentiment analysis ignore the contributions of individual words and their grammatical connections because of a lack of annotated data and algorithms.
Approach: They present a dataset by a word-level, syntactically informed crowdsourcing technique and propose a supervised technique with a suffix-tree data structure to model syntactic relationships.
Outcome: The proposed method can uncover grammatical patterns that connect opinion expressing words and target entities and outperforms syntactically uninformed baselines.