It all started with social media. Like many Facebook fans of my age, a significant part of my life was spent on social media. As we take knowledge from the infinite pool of cyberspace, cyberspace, in return, instilled appalling social habits, and my social interactions simply became competitions of who can glue eyes to their screen the most. Consequently, for me (and my 819 friends), my speech patterns rescinded to a level akin to OMG LOL I have to get to class. I lived in social media, knowing it inside and out … In sophomore year, I had an opportunity to put my social media expertise to some use as a paid summer intern at a Silicon Valley startup that automatically tracks public opinions and sentiments from social media. Their system uses natural language technology to do sentiment analysis of consumer opinions about a brand or topic … My initial job was to incorporate social media jargon into the system, especially the emotional expressions from Urban Dictionary. I was also assigned to test entries from Facebook fan pages, sorting positive sentiment from negative. I soon immersed myself into my work routine but noticed that the system always disregarded smiley faces (emoticons) as these are things beyond words, extra-linguistic symbols. As visible representations of emotion, isn’t that a missed opportunity to help gather sentiment? A happy face like :) usually denotes a positive tone of sentiment while a sad face :( a negative tone. Intuitively, it should help the system for the purpose of sentiment analysis … This research presents a novel study of how emoticons can help sentiment analysis precision. Data analysis shows that emoticons alone cannot determine sentiments towards a brand and they can only be used together with other evidence. Further study has discovered a use of emoticons as counter evidence to block glaring errors in sentiment analysis …

Read More

• Mathematics
• Computer Science