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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ ³í¹®Áö

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Current Result Document : 17 / 17 ÀÌÀü°Ç ÀÌÀü°Ç

ÇѱÛÁ¦¸ñ(Korean Title) Æ®À§Å͸¦ ÀÌ¿ëÇÑ À̺¥Æ® °¨Áö½Ã½ºÅÛ
¿µ¹®Á¦¸ñ(English Title) Event Detection System Using Twitter Data
ÀúÀÚ(Author) ¹Úżö   Á¤¿Á¶õ   Tae Soo Park   Ok-Ran Jeong  
¿ø¹®¼ö·Ïó(Citation) VOL 17 NO. 06 PP. 0153 ~ 0158 (2016. 12)
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(Korean Abstract)
ÃÖ±Ù ¼Ò¼È ³×Æ®¿öÅ© »ç¿ëÀÚµéÀÌ ´Ã¾î³ª¸é¼­, °¢ Áö¿ª¿¡¼­ °ü½É ¹Þ°í ÀÖ´Â »çȸÀûÀÎ À̽´³ª ÀçÇØ µî°ú °°Àº À̺¥Æ®¿¡ ´ëÇÑ Á¤º¸µéÀÌ ¼Ò¼È ¹Ìµð¾î »çÀÌÆ®¸¦ ÅëÇØ ½Ç½Ã°£À¸·Î ºü¸£°Ô ´ë·®À¸·Î °Ô½ÃµÇ°í ÀÖÀ¸¸ç, »çȸÀû ÆıÞÈ¿°úµµ ¸Å¿ì Ä¿Áö°í ÀÖ´Ù. º» ³í¹®¿¡¼­´Â Áö¿ªÁ¤º¸¸¦ °¡Áø Æ®À§ÅÍ µ¥ÀÌÅ͸¦ ÀÌ¿ëÇÏ¿© ƯÁ¤½Ã°£, Áö¿ª¿¡ »ç¿ëÀÚµéÀÌ °ü½ÉÀ» °¡Áö°í ÀÖ´Â À̺¥Æ®¸¦ ŽÁöÇÏ´Â ¹æ¹ýÀ» Á¦¾ÈÇÏ°íÀÚ ÇÑ´Ù. À̸¦ À§ÇØ Æ®À§ÅÍ ½ºÆ®¸®¹Ö API¸¦ ÀÌ¿ëÇØ µ¥ÀÌÅ͸¦ ¼öÁýÇÏ°í, Æ®À­ÀÇ Å°¿öµåµéÀÇ ½Ã°£¿¡ µû¸¥ ºóµµ¼ö¸¦ ºÐ¼®ÇÏ¿© Á¤»óÀûÀÎ ÆÐÅÏ°ú ´Ù¸¥ ÆÐÅÏÀ» °¡Áø Å°¿öµå¸¦ À̺¥Æ®·Î ÃßÃâÇÏ°í, °°Àº À̺¥Æ®¿¡ ´ëÇÑ Å°¿öµåµéÀ» ±ºÁýÈ­ Çϱâ À§ÇØ co-occurrence ±×·¡ÇÁ¸¦ ÀÌ¿ëÇÏ¿© À̺¥Æ® °¨Áö ½Ã½ºÅÛÀ» ±¸ÇöÇÏ¿´´Ù. ±×¸®°í ½ÇÇèÀ» ÅëÇØ Á¦¾ÈÇÑ ±â¹ýÀÇ À¯È¿¼ºÀ» °ËÁõÇÑ´Ù.
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(English Abstract)
As the number of social network users increases, the information on event such as social issues and disasters receiving attention in each region is promptly posted by the bucket through social media site in real time, and its social ripple effect becomes huge. This study proposes a detection method of events that draw attention from users in specific region at specific time by using twitter data with regional information. In order to collect Twitter data, we use Twitter Streaming API. After collecting data, We implemented event detection system by analyze the frequency of a keyword which contained in a twit in a particular time and clustering the keywords that describes same event by exploiting keywords¡¯ co-occurrence graph. Finally, we evaluates the validity of our method through experiments.

Å°¿öµå(Keyword) À̺¥Æ®°¨Áö   ¼Ò¼È³×Æ®¿öÅ©   ¼Ò¼È¹Ìµð¾îÄÜÅÙÃ÷   Event Detection   Social Network   Social Media Contents  
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