A Multilevel Approach to Sentiment Analysis of Figurative Language in Twitter

[EN] Commendable amount of work has been attempted in the field of Sentiment Analysis or Opinion Mining from natural language texts and Twitter texts. One of the main goals in such tasks is to assign polarities (positive or negative) to a piece of text. But, at the same time, one of the important as well as difficult issues is how to assign the degree of positivity or negativity to certain texts. The answer becomes more complex when we perform a similar task on figurative language texts collected from Twitter. Figurative language devices such as irony and sarcasm contain an intentional secondary or extended meaning hidden within the expressions. In this paper we present a novel approach to identify the degree of the sentiment (fine grained in an 11-point scale) for the figurative language texts. We used several semantic features such as sentiment and intensifiers as well as we introduced sentiment abruptness, which measures the variation of sentiment from positive to negative or vice versa. We trained our systems at multiple levels to achieve the maximum cosine similarity of 0.823 and minimum mean square error of 2.170.The work reported in this paper is supported by a grant from the project “CLIA System Phase II” funded by Department of Electronics and Information Technology (DeitY), Ministry of Communications and Information Technology (MCIT), Government of India. The work of the fourth author is also supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the grant ALMAPATER (PrometeoII/2014/030).Gopal Patra, B.; Mazumda, S.; Das, D.; Rosso, P.; Bandyopadhyay, S. (2018). A Multilevel Approach to Sentiment Analysis of Figurative Language in Twitter. 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The studies of self-replication in chemical viz. non-enzymatic model systems have gained significance in recent years for an improved understanding of the origin of life. This article briefly reviews the progress achieved, mainly during the past decade, from organic and bioorganic chemists point of view (1)

Templates, autocatalysis and molecular replication

The studies of self-replication in chemical viz. non-enzymatic model systems have gained significance in recent years for an improved understanding of the origin of life. This article briefly reviews the progress achieved, mainly during the past decade, from organic and bioorganic chemists point of view (1)