The evaluative character of a word is called its semantic orientation (SO). A positive SO indicates desirability (e.g. Good, Honest) and a negative SO indicates undesirability (e.g., Bad, Ugly). This paper presents a method, based on Turney (2003), for inferring the SO of a word from its statistical association with strongly-polarized words and morphemes in Chinese. It is noted that morphemes are much less numerous than words, and that also a small number of fundamental morphemes may be used in the modified system to great advantage. The algorithm was tested on 1,249 words (604 positive and 645 negative) in a corpus of 34 million words, and was run with 20 and 40 polarized words respectively, giving a high precision (79.96% to 81.05%), but a low recall (45.56% to 59.57%). The algorithm was then run with 20 polarized morphemes, or single characters, in the same corpus, giving a high precision of 80.23% and a high recall of 85.03%. We concluded that morphemes in Chinese, as in any language, constitute a distinct sub-lexical unit which, though small in number, has greater linguistic significance than words, as seen by the significant enhancement of results with a much smaller corpus than that required by Turney.
REF presents a method for inferring the semantic orientation of a Chinese word from their association with stronglypolarized Chinese morphemes.
Morpheme-Based Derivation Of Bipolar Semantic Orientation Of Chinese Words
International Conference On Computational Linguistics
Computer Science