Cost and Benefit of Using Word Net Senses for Sentiment Analysis

Typically, accuracy is used to represent the performance of an NLP system. However, accuracy attainment is a function of investment in annotation. Typically, the more the amount and sophistication of annotation, higher is the accuracy. However, a moot question is is the accuracy improvement commensurate with the cost incurred in annotation? We present an economic model to assess the marginal benefit accruing from increase in cost of annotation. In particular, as a case in point we have chosen the Sentiment Analysis (SA) problem. In SA, documents normally are polarity classified by running them through classifiers trained on document vectors constructed from lexeme features, i.e., words. If, however, instead of words, one uses word senses (synset ids in wordnets) as features, the accuracy improves dramatically. But is this improvement significant enough to justify the cost of annotation? This question, to the best of our knowledge, has not been investigated with the seriousness it deserves. We perform a cost benefit study based on a vendor-machine model. By setting up a cost price, selling price and profit scenario, we show that although extra cost is incurred in sense annotation, the profit margin is high, justifying the cost. Additionally we show that a system that uses sense annotation achieves a break even point earlier than the system that does not use sense annotation. Our study is timely in the current NLP scenario of ML applied to richly annotated data, frequently obtained through crowd-sourcing which involves monetary investment.