Big Data seems to be at the peak of its hype cycle these days and I have some issues with it. In the “My issues with Big Data” series I will explore a couple of these. First up: Sentiment.
Sentiment analysis concerns itself about discovering customers’ feelings about something we care about, such as a brand. One of the selling points of Big Data has been that this analysis can be done by machines on massive data amounts.
Apart from the fact that I suspect its far more cost efficient to simply do a good old survey on how the brand / marketing campaign / product is perceived, I have some very practical concerns about the feasibility of the whole concept. Being a simple guy, I think the best way to illustrate this is by a practical example. Let us try to manually “mine” customer sentiment about a well known brand: Coca Cola. Our Big Data source will be Twitter.
Doing a search for “Coca Cola” yields, at the time of this writing, the following first eleven results:
The only way I can think of to discover sentiment in these tweets is to look for positively and negatively charged words / phrases and do a count. As far as I can tell these are the tweets with words that can be interpreted positively:
- Jump as in “jumping as a move done in happiness” in Coca-Cola’s Thailand sales jump 24%
- Amazing and 🙂 in Amazing Coke wall clock 🙂
- Crush as in “being in love” and 🙂 in You have a crush? — Nope, I don’t have a crush but I have coca-cola 🙂
- Brilliant in This is about as brilliant as “New Coke” was years ago. Coca-Cola Debuts “Life” Brand
- Highlights as in “The highlights of the evening were…” in Coca-Cola debuts Life brand, highlights deadlines for regular coke
- Cool in A cool Coca Cola delivery truck in Knoxville, 1909
- Honest in But you know why @Honest isn’t coming for @HonestTea? B/c Honest Tea is owned by Coca Cola and they know they’d lose
In other words: Seven of eleven tweets contain words that have a positive ring to them. The first thing that comes to mind when seeing this is: Is this good or bad? I have no idea. Maybe if we create some kind of ratio between posts with positive words versus negative words we will get a feeling for whether or not the public feels good about Coca Cola. So lets count the negative ones:
- Drunk as “Intoxicated” in 12% of all the Coca-Cola in America is drunk at breakfast
- Crush as in “I will crush you” in You have a crush? — Nope, I don’t have a crush but I have coca-cola 🙂
- Lose in But you know why @Honest isn’t coming for @HonestTea? B/c Honest Tea is owned by Coca Cola and they know they’d lose
Three negative tweets right? Wait a minute. Two of those posts are also in the positive list! The first one because crush can be interpreted both positively and negatively and the second one because the tweet contains both a positive and a negative word. We need to refine our algorithm to deal with this. The solution is quite simple. For each tweet we need to keep a score of positive and negative words. Ambiguous words can be removed because they would add to both the positive and negative scores. Tweets with ties need to be removed as they are neutral. The effect on our sample is that both “You have a crush..” and “But you know why @Honest..” tweets have to be removed from the count. The end result is that of the eleven tweets two have to be taken out due to the above ambiguity and three tweets need to removed because they contain neither positive nor negative words. So our ratio would be 5 positive / (5 positive + 1 negative) = 83% of tweets are favorable towards the Coca Cola brand. Right?
Of course not. Lets stop thinking like a machine now and look at the tweets with our human cognitive sense:
- 12% of all the Coca-Cola in America is drunk at breakfast: Obviously this has nothing to do with being drunk but rather a depressing health statistic.
- Coca-Cola’s Thailand sales jump 24%: This is not a sentiment, its a positive financial news flash.
- Amazing Coke wall clock :): Does this have something to do with liking the Coca Cola brand or liking the clock? Probably the latter.
- You have a crush? — Nope, I don’t have a crush but I have coca-cola :): This might actually be positive (but remember it was removed due to ambiguity)
- This is about as brilliant as “New Coke” was years ago. Coca-Cola Debuts “Life” Brand: At first I thought this would be a perfect sentiment tweet. An unambiguous positive term tightly linked to the Coca Cola brand. However I did not know anything about “New Coke” so I did a quick search. Uh oh. The author of the tweet seems to be ironic. Good luck interpreting that correctly, machine learning algorithm!
- Coca-Cola debuts Life brand, highlights deadlines for regular coke: “Highlights” is not used as we thought. Its used as “emphasize”, a neutral term, not a positive one.
- A cool Coca Cola delivery truck in Knoxville, 1909: Same problem as with the clock. Is the tweet positive about Coca Cola or about the physical truck? Probably the latter.
- But you know why @Honest isn’t coming for @HonestTea? B/c Honest Tea is owned by Coca Cola and they know they’d lose: I am not sure what to think of this. I do not know who or what either @Honest or @HonestTea are/is. I doubt a machine would know better.
While my “algorithm” and output in this example are quite simplistic it still illustrates my point: Sentiment analysis is very tricky. As far as I can tell this analysis has invalidated every single tweet from my (admittedly very limited) sample. Add to this the tweets that did not contain any words indicating sentiment and you have a pretty bleak picture of what automated sentiment analysis can do.
Disagree? Feel free to comment!
Some additional reading on sentiment analysis:
- Here is a research paper detailing a more sophisticated algorithm than the one I exemplify the challenges with sentiment analysis with. The findings seem encouraging but I am still not convinced of the viability of this commercially.
- Here are instructions of how to use Google’s infrastructure and API’s for sentiment analysis.
- Here is a piece in The Guardian that looks at this a little more broadly.