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Table 4.1 Twittratr.com uses these and more in their bag-ofwords to rate tweets for sentiment
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Speci c Sentiment Words from Twittratr.com Positive Words Negative Words http://docs.google.com/Doc id =df5m8zwp 92gvtfm3d9 Woo quite amazing looking forward to damn good frickin ruled frickin rules way to go cute comeback not suck prop kinda impressed props come on congratulation gtd proud thanks can help thanks! pumped integrate really like loves it yay amazing epic ail ail good luck fail life saver http://docs.google.com/Doc id =df5m8zwp 93gd2mhkd7 irritating not that good suck lying duplicity angered dumbfounding dumbifying not as good not impressed stomach it pw pwns pwnd pwning in a bad way horrifying wrong failing fallen way behind fallen behind lose fallen self-deprecating hunker down duh got killed by hated us only works in safari must have ie fuming and frothing
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piece of cake good thing hawt hawtness highly positive my hero LOL :) ;) :-) ;-) :D ;] :] :p ;p
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heavy buggy unusable nothing is is great until don t support despise sucks problems not working fuming an issue WTF :( :-(
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Even when we have enough grist, suggesting that every statement that includes the phrase is a great deal is necessarily positive is going to lead us into trouble. As humans, we can instantly classify these three examples from Lillian Lee (www.cs.cornell.edu/home/llee/talks/llee-aaai08.pdf ): 1. This laptop is a great deal. 2. A great deal of media attention surrounded the release of the new laptop. 3. This laptop is a great deal . . . and I ve got a nice bridge you might be interested in. Perhaps machines can eventually be taught. But who will teach them Machines have such a hard time with this because humans do, too.
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What We Have Here Is a Failure to Communicate How Sad is That
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In some very straightforward research documented in a very straightforward paper called Detecting Sadness in 140 Characters: Sentiment Analysis and Mourning Michael Jackson on Twitter, Elsa Kim and Sam Gilbert et al. proved that this is too hard even for humans. The task was basic: Michael Jackson s death created an emotional outpouring of unprecedented magnitude on Twitter. In this report, we examine 1,860,427 tweets about Jackson s death in order to test various methods of sentiment analysis and gain insights into how people express emotion on Twitter. They wanted to know if tweets about Michael Jackson that included the word sad were really expressing sadness about Michael Jackson s passing. Sounds a bit simplistic, right Hold on to your socks. First, they took 44,383 tweets and analyzed them against the ANEW dataset. The Affective Norms for English Words (ANEW) dataset contains normative emotional ratings for 1034 English words. Each word in the dataset is associated with a rating of 1 9 along each of three dimensions of emotional affect: valence (pleasure vs. displeasure), arousal (excitement vs. calmness), and dominance (strength vs. weakness) (Bradley and Lang, 1999).
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The scoring of the tweets along those dimensions is documented in this 15-page PDF but it wasn t the red ashing light for me. That came when they then randomly selected 346 tweets and asked six human coders to sort them. The coders were asked to tag each one:
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Y yes; the person who created this tweet is expressing sadness N no; the person who created this tweet is not expressing sadness M mixed; the person who created this tweet expresses sadness as well as another con icting emotion U unclear; the tweet in question is spam, is not in English, or is otherwise impossible to interpret with respect to sentiment.
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How do you use the word sad in a way that does not express sadness Turns out to be easy. Let s start with calm and sad: Michael Jackson s death is a sad loss . . . thoughts and prayers go out to his family. Next, hyperemotional: Michael Jackson Died!! whatt im saddened . . . deeply sad:( Then, sad about the results:
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