What are Sentiment, Affect, and Emotion? Applying the Methodology of Michael Zock to Sentiment Analysis

• Published in: Language Production, Cognition, and the Lexicon Vol. 48; pp. 13 - 24
• Main Author: Hovy, Eduard H.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 01.01.2015; Springer International Publishing
• Series: Text, Speech and Language Technology
• Subjects: Cognitive view on opinion; Computational linguistics; Computational sentiment determination; HUMANITIES; Opinion mining; Psycholinguistics; Sentiment analysis
• ISBN: 3319080423; 9783319080420
• ISSN: 1386-291X
• DOI: 10.1007/978-3-319-08043-7_2

In Natural Language Processing or Computational Linguistics (NLP or CL), researchers assume almost universally that speakers hold some affective value or sentiment with regard to (some aspects of) a topic such as a film or camera, that this sentiment has a fixed value (typically, something like good or bad), and that the sentiment is expressed in text through a word or small combination of words. However, one finds in the NLP literature essentially no discussion about what ‘sentiment’ or ‘opinion’ really is, how it is expressed in actual language usage, how the expressing words are organized and found in the lexicon, and how in fact one can empirically verify cognitive claims, if any, implied in or assumed by an NLP implementation. Even the Wikipedia definition, which is a little more careful than most of the NLP literature, uses words like “polarity”, “affective state”, and “emotional effect” without definition. In this situation we can usefully try to duplicate Michael’s mindset and approach. What do people actually do? How does what they do illustrate the complexities of the problem and disclose unusual and interesting aspects that computer scientists are simply blind to? In this paper I first provide interesting examples of real-world usage, then explore some definitions of sentiment, affect, opinion, and emotion, and conclude with a few suggestions for how computational studies might address the problem in a more informed way. I hope in the paper to follow the spirit of Michael’s research, in recognizing that there is much more to language usage than simply making some computer system mimic some annotated corpus, and that one can learn valuable lessons for NLP by looking at what people do when they make language.