Investigating Metaphorical Language in Sentiment Analysis

Abstract

Intuition dictates that figurative language and especially metaphorical expressions should convey sentiment. It is the aim of this work to validate this intuition by showing that figurative language (metaphors) appearing in a sentence drive the polarity of that sentence. Towards this target, the current article proposes an approach for sentiment analysis of sentences where figurative language plays a dominant role. This approach applies Word Sense Disambiguation aiming to assign polarity to word senses rather than tokens. Sentence polarity is determined using the individual polarities for metaphorical senses as well as other contextual information. Experimental evaluation shows that the proposed method achieves high scores in comparison with other state-of-the-art approaches tested on the same corpora. Finally, experimental results provide supportive evidence that this method is also well suited for corpora consisting of literal and figurative language sentences. ACM Reference Format:Rentoumi, V., Vouros, G. A., Karkaletsis, V., and Moser, A. 2012. Investigating metaphorical language in sentiment analysis: A sense-to-sentiment perspective.

Word Embedding and WordNet Based Metaphor Identification and Interpretation

“…Linguistically, metaphor is defined as a language expression that uses one or several words to represent another concept, rather than taking their literal meanings of the given words in the context (Lagerwerf and Meijers, 2008). Computational metaphor processing refers to modelling non-literal expressions (e.g., metaphor, metonymy, and personification) and is useful for improving many NLP tasks such as Machine Translation (MT) and Sentiment Analysis (Rentoumi et al, 2012). For instance, Google Translate failed in translating devour within a sentence, "She devoured his novels."…”

Main Concepts, State of the Art and Future Research Questions in Sentiment Analysis

“…A system combining together concept-level sentiment analysis and opinion mining lexicon-based and learning-based approaches is proposed in [48]. Dealing with metaphoric language is hard, and some researchers have spent some time suggesting how to address the problem [9,53]. Recognising irony and sarcasm are tough topics as well, and some work has been done in this area [62].…”

Contextual Modulation for Relation-Level Metaphor Identification

“…Over the last couple of years, there has been an increasing interest towards metaphor processing and its applications, either as part of natural language processing (NLP) tasks such as machine translation (Koglin and Cunha, 2019), text simplification (Wolska and Clausen, 2017;Clausen and Nastase, 2019) and sentiment analysis (Rentoumi et al, 2012) or in more general discourse analysis use cases such as in analysing political discourse (Charteris-Black, 2011), financial reporting (Ho and Cheng, 2016) and health communication (Semino et al, 2018).…”