Multimodal analysis of a sample of political posters in Ireland during and after the Celtic Tiger

The aim of this research paper is to analyse the key political posters made for the campaigns of Irish political party Fianna Fáil framed in the Celtic Tiger (1997-2008) and post-Celtic Tiger years (2009-2012). I will then focus on the four posters of the candidate in the elections that took place in 1997, 2002, 2007 and 2011 with the intention of observing first how the leader is represented, and later on pinpointing the similarities and possible differences between each. This is important in order to observe the main linguistic and visual strategies used to persuade the audience to vote that party and to highlight the power of the politician. Critical discourse analysis tools will be helpful to identify the main discursive strategies employed to persuade the Irish population to vote in a certain direction. Van Leeuwen’s (2008) social actor theory will facilitate the understanding of how participants are represented in the corpus under analysis. Finally, the main tools of Kress and van Leeuwen’s visual grammar (2006) will be applied for the analysis of the images. The study reveals that politicians are represented in a consistently positive way, with status and formal appearance so that people are persuaded to vote for the party they represent because they trust them as political leaders. The study, thus, points out that the poster is a powerful tool used in election campaigns to highlight the power of political parties.

Sentiment classification for early detection of health alerts in the chemical textile domain

In the chemical textile domain experts have to analyse chemical components and substances that might be harmful for their usage in clothing and textiles. Part of this analysis is performed searching opinions and reports people have expressed concerning these products in the Social Web. However, this type of information on the Internet is not as frequent for this domain as for others, so its detection and classification is difficult and time-consuming. Consequently, problems associated to the use of chemical substances in textiles may not be detected early enough, and could lead to health problems, such as allergies or burns. In this paper, we propose a framework able to detect, retrieve, and classify subjective sentences related to the chemical textile domain, that could be integrated into a wider health surveillance system. We also describe the creation of several datasets with opinions from this domain, the experiments performed using machine learning techniques and different lexical resources such as WordNet, and the evaluation focusing on the sentiment classification, and complaint detection (i.e., negativity). Despite the challenges involved in this domain, our approach obtains promising results with an F-score of 65% for polarity classification and 82% for complaint detection.