Thesis (M.A.)--Central Connecticut State University, 2001. / Thesis advisor: Joanne DePlacido. " ... in partial fulfillment of the requirements for the degree of Master of Arts in Psychology." Includes bibliographical references (leaves 33-40). Also available via the World Wide Web.

The International Affective Picture System (IAPS) was developed in an attempt to provide a standardised tool to elicit and measure emotions for research purposes. The IAPS is unique, in that it is completely pictorially based. An emotional response is stimulated by the pictures, which are then used to measure the emotional response. This has obvious benefits in South Africa. The overall aim of this study was to conduct a cross-cultural exploration of the IAPS, with a sample of South African university students, in order to come to an initial understanding of the measure’s performance within the South African context. A quantitative methodology was used, in order to reach the research aim and objectives. The overall research approach was exploratory and descriptive in nature and the actual data gathering consisted of a single measurement instance. The procedure for this study was based on that used by the developers of the IAPS to norm the instrument. Convenience sampling was used, resulting in a total sample of 169 participants, 31 male, and 136 female. For analysis purposes, participants were grouped according to a race and language combination, thus operationalising ethnicity. This resulted in four primary ethnic groupings. In summary, it was found that a large number of items seem to travel well in terms of equivalence. The correlations achieved and affective space plot are consistent with that outlined in the IAPS instruction manual, and are similar to international studies using the same procedure. However, when items were examined in greater detail, statistically significant differences raised concerns about the level of equivalence and suggested that not all items travel equally well. Similarly, although many items were statistically similar between the South African ethnic groups, differences were also found on specific items.

Identifying sentiment in a low-resource language is essential for understanding opinions internationally and for responding to the urgent needs of locals affected by disaster incidents in different world regions. While tools and resources for recognizing sentiment in high-resource languages are plentiful, determining the most effective methods for achieving this task in a low-resource language which lacks annotated data is still an open research question. Most existing approaches for cross-lingual sentiment analysis to date have relied on high-resource machine translation systems, large amounts of parallel data, or resources only available for Indo-European languages. This work presents methods, resources, and strategies for identifying sentiment cross-lingually in a low-resource language. We introduce a cross-lingual sentiment model which can be trained on a high-resource language and applied directly to a low-resource language. The model offers the feature of lexicalizing the training data using a bilingual dictionary, but can perform well without any translation into the target language. Through an extensive experimental analysis, evaluated on 17 target languages, we show that the model performs well with bilingual word vectors pre-trained on an appropriate translation corpus. We compare in-genre and in-domain parallel corpora, out-of-domain parallel corpora, in-domain comparable corpora, and monolingual corpora, and show that a relatively small, in-domain parallel corpus works best as a transfer medium if it is available. We describe the conditions under which other resources and embedding generation methods are successful, and these include our strategies for leveraging in-domain comparable corpora for cross-lingual sentiment analysis. To enhance the ability of the cross-lingual model to identify sentiment in the target language, we present new feature representations for sentiment analysis that are incorporated in the cross-lingual model: bilingual sentiment embeddings that are used to create bilingual sentiment scores, and a method for updating the sentiment embeddings during training by lexicalization of the target language. This feature configuration works best for the largest number of target languages in both untargeted and targeted cross-lingual sentiment experiments. The cross-lingual model is studied further by evaluating the role of the source language, which has traditionally been assumed to be English. We build cross-lingual models using 15 source languages, including two non-European and non-Indo-European source languages: Arabic and Chinese. We show that language families play an important role in the performance of the model, as does the morphological complexity of the source language. In the last part of the work, we focus on sentiment analysis towards targets. We study Arabic as a representative morphologically complex language and develop models and morphological representation features for identifying entity targets and sentiment expressed towards them in Arabic open-domain text. Finally, we adapt our cross-lingual sentiment models for the detection of sentiment towards targets. Through cross-lingual experiments on Arabic and English, we demonstrate that our findings regarding resources, features, and language also hold true for the transfer of targeted sentiment.

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This thesis challenges dualistic human and animal ontologies by interpreting science fiction (sf) literature, and argues that whereas words can equivocate and obscure meaning, bodies do not lie. Linguistics and semiology extend the definition of "language" to include human and nonhuman gestures and movement, and posthumanist theory expands definitions of "human" and "animal" to explore species boundaries. Scrutinizing opposing dualisms ultimately questions Western epistemology and authority, allowing for an exploration of embodied animal communications within the larger discourse on species and speciesism. This perspective results in a more comprehensive understanding of the interdependence of all species: human, animal, and "other." Although the fictional texts I employ use fantastic elements to posit hypothetical realities, current scientific research reveals that communication with nonhuman animals is indeed possible. / by Jennifer K. Cox. / Thesis (M.A.)--Florida Atlantic University, 2013. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.