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    Graph-based approaches for semi-supervised and cross-domain sentiment analysis

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    Authors
    Ponomareva, Natalia
    Advisors
    Thelwall, Mike
    Issue Date
    2014
    
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    Abstract
    The rapid development of Internet technologies has resulted in a sharp increase in the number of Internet users who create content online. Usergenerated content often represents people's opinions, thoughts, speculations and sentiments and is a valuable source of information for companies, organisations and individual users. This has led to the emergence of the eld of sentiment analysis, which deals with the automatic extraction and classi cation of sentiments expressed in texts. Sentiment analysis has been intensively researched over the last ten years, but there are still many issues to be addressed. One of the main problems is the lack of labelled data necessary to carry out precise supervised sentiment classi cation. In response, research has moved towards developing semi-supervised and crossdomain techniques. Semi-supervised approaches still need some labelled data and their e ectiveness is largely determined by the amount of these data, whereas cross-domain approaches usually perform poorly if training data are very di erent from test data. The majority of research on sentiment classi cation deals with the binary classi cation problem, although for many practical applications this rather coarse sentiment scale is not su cient. Therefore, it is crucial to design methods which are able to perform accurate multiclass sentiment classi cation. iii The aims of this thesis are to address the problem of limited availability of data in sentiment analysis and to advance research in semi-supervised and cross-domain approaches for sentiment classi cation, considering both binary and multiclass sentiment scales. We adopt graph-based learning as our main method and explore the most popular and widely used graph-based algorithm, label propagation. We investigate various ways of designing sentiment graphs and propose a new similarity measure which is unsupervised, easy to compute, does not require deep linguistic analysis and, most importantly, provides a good estimate for sentiment similarity as proved by intrinsic and extrinsic evaluations. The main contribution of this thesis is the development and evaluation of a graph-based sentiment analysis system that a) can cope with the challenges of limited data availability by using semi-supervised and crossdomain approaches b) is able to perform multiclass classi cation and c) achieves highly accurate results which are superior to those of most stateof- the-art semi-supervised and cross-domain systems. We systematically analyse and compare semi-supervised and cross-domain approaches in the graph-based framework and propose recommendations for selecting the most pertinent learning approach given the data available. Our recommendations are based on two domain characteristics, domain similarity and domain complexity, which were shown to have a signi cant impact on semi-supervised and cross-domain performance.
    Publisher
    University of Wolverhampton
    URI
    http://hdl.handle.net/2436/323990
    Type
    Thesis or dissertation
    Language
    en
    Description
    A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy
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