Abstract
The detection of expressions of sentiment in online text has become a popular Natural Language Processing application. The task is commonly defined as identifying the words or phrases in a given fragment of text in which the reader understands that the author expresses some person’s positive, negative, or perhaps neutral attitude toward a topic. These four elements—expression words, attitude holder, topic, and attitude value—have evolved with hardly any discussion in the literature about their foundation or nature. Specifically, the use of two (or three) attitude values is far more simplistic than many examples of real language show. In this paper we ask: where do sentiments come from? We focus on two basic sources of human attitude—the holder’s non-logical/emotional preferences and the fulfillment of the holder’s goals. After exploring each source we provide a notional algorithm sketch and examples of how sentiment systems could provide richer and more realistic accounts of sentiment in text.
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Notes
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However, putting them aside them doesn’t mean that we don’t need to explore and explain these complex situations. On the contrary, these situations are essential and fundamental to the understanding of opinion and sentiment, but requires deeper and more systematic exploration in psychology, cognitive science, and AI.
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These reviews were originally from yelp reviews and revised by the authors for illustration purposes.
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Li, J., Hovy, E. (2017). Reflections on Sentiment/Opinion Analysis. In: Cambria, E., Das, D., Bandyopadhyay, S., Feraco, A. (eds) A Practical Guide to Sentiment Analysis. Socio-Affective Computing, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-55394-8_3
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