Abstract
For more than 10 years now, Sentiment Analysis has enjoyed enormous popularity in Computational Linguistics, one main reason being its great potential for practical applications, predominantly (but not only) for industrial purposes. We observe a tendency that early work referred to certain theoretical notions of Subjectivity, whereas a lot of the later approaches follow the ‘engineering’ perspective that can include using terminology somewhat indiscriminately and are not aiming at making progress with the underlying theoretical issues. In this paper, we first survey some important notions surrounding “Subjectivity” in Linguistics and Psychology, trying to broaden the perspective of standard opinion analysis. Thereafter, we take a snapshot of the state of the art in computational Sentiment Analysis, as it has developed since roughly 2000. Combining these two viewpoints leads us to assessing the gap between the broader notion of Subjectivity Analysis and the subfields that language technology research tends to focus on. We suggest a few potential research directions that could help narrowing this gap.
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Notes
- 1.
We provide just a minimal property of sentiment at this point, which goes beyond coarse-grained Sentiment Analysis, but it is deliberately a rather abstract description. We come back to this issue in Sect. 4.
- 2.
Ortony et al. [19, pp. 29–32] give examples for non-emotional events giving rise to emotions. Yet, the distinction is clear.
- 3.
Notice the identity to the linguistic term discussed in the previous section (Martin/White). There seems to be no direct (established) connection between the two approaches.
- 4.
Note that the use of the term ‘want’ suggests Subjectivity.
- 5.
In this case, the authors intention is to specify factuality, which relates to ‘Evidentiality’ and ‘Veridicity’ (cf. Sect. 2.2.1).
- 6.
All Sentences are taken from http://www.amazon.com/Bosch-SHP65T55UC-Stainless-Integrated-Dishwasher/dp/B00CWX0KDA/ref=sr_1_2?ie=UTF8&qid=1401714426&sr=8-2&keywords=dishwasher.
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We thank our anonymous reviewers for detailed and constructive criticism, which enabled us to improve this work at several points.
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Sonntag, J., Stede, M. (2014). Sentiment Analysis: What’s Your Opinion?. In: Biemann, C., Mehler, A. (eds) Text Mining. Theory and Applications of Natural Language Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-12655-5_9
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