An Experience in Automatically Building Lexicons for Affective Computing in Multiple Target Languages

Affective Computing in text attempts to identify the emotional charge reflected in it, trying to analyse the moods transmitted while writing. There are several techniques and approaches to perform Affective Computing in texts, but lexicons are their common point. However, it is difficult to find solutions for specific languages different from English. Thus, this article presents an experience in automatically generating lexicons to perform Affective Computing following a multiple-target languages approach. The experience starts with some initial seeds of words in English that define the emotions we want to identify. It then expands them as much as possible with related words in a bootstrapping process and finally obtains a lexicon by processing the context sentences from parallel translated text where the terms have been used. We have checked the resulting lexicons by conducting an exploratory analysis of the affective fingerprint on a parallel corpus with books translated from and to different languages. The obtained results look promising, showing really similar affective fingerprints in different language translations for the same books.The authors are grateful to anonymous referees for providing constructive comments and helping to improve the contents of this manuscript. The research of the authorswas supported in part by theARTEMISJoint Undertaking under grant agreement no. 295373 (project nSafeCer) and by National funding. The research of Ricardo J. Rodríguez was also supported in part by EU Horizon 2020 research and innovation programme under grant agreement no. 644869 (DICE) and by Spanish MINECO project CyCriSec (TIN2014-58457-R). The research of Clara Benac Earle was also supported by Spanish MINECO project STRONGSOFT (TIN2012-39391-C04-02) and by the Madrid Regional Government project nGreens (S2013/ICE-2731)

Subjectivity Analysis In Opinion Mining - A Systematic Literature Review

Subjectivity analysis determines existence of subjectivity in text using subjective clues.It is the first task in opinion mining process.The difference between subjectivity analysis and polarity determination is the latter process subjective text to determine the orientation as positive or negative.There were many techniques used to solve the problem of segregating subjective and objective text.This paper used systematic literature review (SLR) to compile the undertaking study in subjective analysis.SLR is a literature review that collects multiple and critically analyse multiple studies to answer the research questions.Eight research questions were drawn for this purpose.Information such as technique,corpus,subjective clues representation and performance were extracted from 97 articles known as primary studies.This information was analysed to identify the strengths and weaknesses of the technique,affecting elements to the performance and missing elements from the subjectivity analysis.The SLR has found that majority of the study are using machine learning approach to identify and learn subjective text due to the nature of subjectivity analysis problem that is viewed as classification problem.The performance of this approach outperformed other approaches though currently it is at satisfactory level.Therefore,more studies are needed to improve the performance of subjectivity analysis