Mixing Metaphors, Modifiers And Affect Towards Sentiment Evaluation

Abstract

The use of figurative language, with a major emphasis on metaphors and a minor emphasis on oxymorons, has been widely accepted as part of everyday language, not just in literary language. Over the last few years, there has also been a large move towards automated sentiment analysis tlu·ough which diverse corporations seek feedback on the sentiment (or affect: emotions, value judgments, etc.) that customers bear towards their goods and services. The need for this feedback has been particularly challenged by the use of social media, which allow the use of non-literal language, including shorthand, abbreviations and emoticons. Begilming with an overview of metaphors, sentiment analysis, modifiers, and how they relate to each other in terms of conveying affect, this thesis examines the accuracy of relying on lexical libraries like SentiWordNet and WordNet in an attempt to extract sentiment-related information on language in discourse. Following a set of empirical studies and experiments, I examined how some existing systems are carrying out analysis of metaphors and oxymorons, and how those systems evaluate metaphors that have made use of modifiers. I demonstrate that modifiers do enhance the sentiment conveyed by metaphors, though their placement within the metaphor, if not done well, can distort the intended meaning, providing a good motivation for non-literal text identification systems to be integrated into existing sentiment analysis systems. I also prove by analysis that SentiWordNet has inherent inaccuracies that introduce errors in sentiment extractions, and recommend that it is crucial to identify non-literal text before sentiment is extracted in order to avoid incorrect judgments