I’m currently reading Logan Dillard’s Masters thesis on sentiment mining titled: “I Can’t Recommend This Paper Highly Enough”: Valence-Shifted Sentences in Sentiment Classification. It’s an interesting read, approaching the space in exactly the right way: looking for types, measuring their distribution and then using that information to improve existing methods. One of the key observations is captured in the table below. 15% of the sentences in the corpus studied contains valence shifters. Of those, 49.4% were valence shifters affecting verb phrases.
|VS Type||Corpus||VS Sentences|
|negated verb phrase||7.4%||49.4%|
|negated noun phrase||3.9%||25.9%|
|negated adj phrase||2.9%||19.3%|
In addition, it was observed that valence shifters are more often used in negative expressions than positive.
Note: valence shifters are lexical items which later (generally invert) the appraisal orientation of an expression; for example ‘not’ in ‘not good’.