Be all and end alls: Natural Language Processing?

I have some doubts about the effectiveness of anything which depends heavily on natural language processing at the moment – I think there’s a lot to interest in the field but I don’t really think it has reached a point of dependability. One of the highest profile – I hesitate to use the word experiment – pieces of work this year, for example, included this comment:

Posts were determined to be positive or negative if they contained at least one positive or negative word, as defined by Linguistic Inquiry and Word Count software (LIWC2007) (9) word counting system, which correlates with self-reported and physiological measures of well-being, and has been used in prior research on emotional expression (7, 8, 10)

(Experimental evidence of massive-scale emotional contagion through social networks, otherwise known as the Facebook emotion study)

Anyway, the reason I am writing about this again today was that this piece from Forbes turned up in my twitter feed and the line which caught my eye was this:

Terms like “overhead bin” and “hate” in the same tweet, for example, might drive down an airline’s raking in the luggage category while “wifi” and “love” might drive up the entertainment category.

Basically, the piece is a bit of a puff piece for a company called Luminoso, and it has as its source this piece from Re/Code. Both pieces are talking about some work Luminoso did to rate airlines according to the sentiment they evoke on twitter.

If you look at the quote from the Facebook study above, the first thing that should step out immediately to you is that under their stated criteria, it is clearly possible for a piece of text to be both positive and negative at the same time. All it has to do is feature one word from each of the positive and negative word lists. Without seeing their data, it is hard to make a call on how much or, whether they checked how frequently, that happened, whether they controlled for it, or whether they excluded. The Forbes quote above likewise is worryingly simplistic in terms of understanding what needs to be done.

This is Luminoso’s description of their methodology. It doesn’t give away very much but given that they claim abilities in a number of languages, I really would not mind seeing more about how they are doing this.

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