A distributional and syntactic approach to fine-grained opinion mining

This thesis contributes to a larger social science research program of analyzing the diffusion of IT innovations. We show how to automatically discriminate portions of text dealing with opinions about innovations by finding {source, target, opinion} triples in text. In this context, we can discern a list of innovations as targets from the domain itself. We can then use this list as an anchor for finding the other two members of the triple at a ``fine-grained'' level---paragraph contexts or less. We first demonstrate a vector space model for finding opinionated contexts in which the innovation targets are mentioned. We can find paragraph-level contexts by searching for an ``expresses-an-opinion-about'' relation between sources and targets using a supervised model with an SVM that uses features derived from a general-purpose subjectivity lexicon and a corpus indexing tool. We show that our algorithm correctly filters the domain relevant subset of subjectivity terms so that they are more highly valued. We then turn to identifying the opinion. Typically, opinions in opinion mining are taken to be positive or negative. We discuss a crowd sourcing technique developed to create the seed data describing human perception of opinion bearing language needed for our supervised learning algorithm. Our user interface successfully limited the meta-subjectivity inherent in the task (``What is an opinion?'') while reliably retrieving relevant opinionated words using labour not expert in the domain. Finally, we developed a new data structure and modeling technique for connecting targets with the correct within-sentence opinionated language. Syntactic relatedness tries (SRTs) contain all paths from a dependency graph of a sentence that connect a target expression to a candidate opinionated word. We use factor graphs to model how far a path through the SRT must be followed in order to connect the right targets to the right words. It turns out that we can correctly label significant portions of these tries with very rudimentary features such as part-of-speech tags and dependency labels with minimal processing. This technique uses the data from the crowdsourcing technique we developed as training data. We conclude by placing our work in the context of a larger sentiment classification pipeline and by describing a model for learning from the data structures produced by our work. This work contributes to computational linguistics by proposing and verifying new data gathering techniques and applying recent developments in machine learning to inference over grammatical structures for highly subjective purposes. It applies a suffix tree-based data structure to model opinion in a specific domain by imposing a restriction on the order in which the data is stored in the structure

Method for Aspect-Based Sentiment Annotation Using Rhetorical Analysis

This paper fills a gap in aspect-based sentiment analysis and aims to present a new method for preparing and analysing texts concerning opinion and generating user-friendly descriptive reports in natural language. We present a comprehensive set of techniques derived from Rhetorical Structure Theory and sentiment analysis to extract aspects from textual opinions and then build an abstractive summary of a set of opinions. Moreover, we propose aspect-aspect graphs to evaluate the importance of aspects and to filter out unimportant ones from the summary. Additionally, the paper presents a prototype solution of data flow with interesting and valuable results. The proposed method's results proved the high accuracy of aspect detection when applied to the gold standard dataset

The good, the bad and the implicit: a comprehensive approach to annotating explicit and implicit sentiment

We present a fine-grained scheme for the annotation of polar sentiment in text, that accounts for explicit sentiment (so-called private states), as well as implicit expressions of sentiment (polar facts). Polar expressions are annotated below sentence level and classified according to their subjectivity status. Additionally, they are linked to one or more targets with a specific polar orientation and intensity. Other components of the annotation scheme include source attribution and the identification and classification of expressions that modify polarity. In previous research, little attention has been given to implicit sentiment, which represents a substantial amount of the polar expressions encountered in our data. An English and Dutch corpus of financial newswire, consisting of over 45,000 words each, was annotated using our scheme. A subset of this corpus was used to conduct an inter-annotator agreement study, which demonstrated that the proposed scheme can be used to reliably annotate explicit and implicit sentiment in real-world textual data, making the created corpora a useful resource for sentiment analysis