Word Count Test

The first test run presented five problems listed in Chapter 5. Each is traceable to a popular tweet or event that is related to IKEA. The "word" http was frequently identified which is to be considered an error in the performance of the program. This is a result of URLs be- ing pasted in the text in the tweets. The parsing algorithm in Watson does not seem to be able to handle them very well and frequently label them as nouns. Furthermore the colon and the two slashes usually following http seem to get lost in the process leaving the rest of the program to interpret every link as the same noun: http. Similarly, Twitter handles are also frequently reported as nouns. This is also likely a result of the way that they are treated in text where they are often used as proper nouns.

The five problems that were presented were:

1. The top problem that was identified was shit. This is not surprising since the way that the sentiment is judged is very much based on individual words. Profanities will by themselves generate a very low sentiment and are likely to quickly rise in severity level if their frequency is high enough. The words that were listed as re- lated to shit were ex along with the Twitter handles @comedypedia, @funnyjoke and @girlsnotebook as well as links represented by the word http.

This problem is likely linked to a popular tweet with the message Oh shit my ex is

on sale at IKEA that was published on March 23, 2015 and received 449 retweets as

of May 10, 2015. The Twitter handles that were related to the problem are probably a result of the message being spread with the inclusion of the Twitter handles were

7. Discussion

deemed to be relevant to the message by the users. As the tweet with the joke relied heavily on an image that image was also linked to consistently when the tweet was spread. Thus that link shows up as http in the results.

Since this problem probably mostly refers to this particular tweet or variants thereof it should probably not be considered an actual problem with IKEAs public relations. Because of that, this problem should be considered a false positive.

2. The second identified problem was ikea. If the algorithm is to be trusted then this should be considered a problem for IKEA. It implies that IKEA was mentioned in a generally negative light in the analyzed tweets. The related words were: putin,

case and read along with the Twitter handle @fartiist. In roughly half of the men-

tions (13107 out of 25577) of ikea, putin was also mentioned. The word case was also mentioned 11470 times along with ikea. This is probably related to a tweet with the message IKEA named their rainbow pillow case Putin I love them so much that was published on March 21, 2015. The tweet was retweeted 11,972 times as of May 10, 2015. In the way that the text gets parsed, the verb named is at the head of the tree and ikea holds a dependency as the subject of named. This means that the word ikea doesn’t have any other words with dependencies to it in this sentence. Which means that it shouldn’t receive any contribution to its sentiment value from this sentence unless the ikea in itself is deemed as positive or negative which it is not. Since the aforementioned tweet is most definitely to be considered positive towards IKEA ("I love [IKEA] so much") it should receive a positive contribution from that tweet. However, Based on the algorithm used it should not receive any contribution to its sentiment value. This means that this tweet is probably not the reason that

ikea is considered such a big problem. The real reason is then overshadowed by the

popularity of the tweet regarding the pillow case. If this is an actual problem is hard to tell.

3. Third on the list of problems is the word hide-and-seek. That word is very interesting because of its rarity. The fact that it appears suggests that it is one specific event or group of events that it references which is exactly what the system is looking for. This problem most likely references massive organised games of hide-and-seek that were being held at IKEA stores that were later banned by IKEA. This sparked controversy on Twitter which might be what the system picked up on. This implies that the system found an actual complaint which is definitely a positive result. 4. Problem number four is the twitterhandle fastcompany. This one seems to relate

to a tweet saying It’s no longer sofa + television = living room. Ikea predicts how

homes will look in 5 years: [URL]. The URL links to an image of a chair and a small

table. This seems like a rather neutral statement and should be considered as a false positive. The only possible source of the negative is the word no.

5. The last problem on the list also seems like an actual problem. Someone bought cheap paintings at IKEA and presented them to art experts who valued them as ex-

7.1 Result

pensive paintings. This spread through twitter as people were criticising the validity of the art experts. The fact that the system found this and identified that experts were the target of the complaints is a success for the system.