Title :
Twitter based TV rating system
Author :
D´Souza, Ankit ; Bathla, Rishabh ; Giri, Nupur
Author_Institution :
Comput. Eng., Univ. of Mumbai, Mumbai, India
Abstract :
TV ratings indicate the popularity of a TV show and these ratings are also used by broadcasters to set their advertising revenue rates. People post their opinions about their favourite shows on social networks like Twitter, Facebook, YouTube, etc. These opinions and comments are a clear indicator of the current number of TV show viewers. The paper presents our work which is towards measuring TV show ratings using Twitter messages pertaining to shows in India. It classifies the relevant tweets based on machine learning and also helps to determine slot prices of ads shown during show time.
Keywords :
Internet; learning (artificial intelligence); social networking (online); telecommunication computing; television broadcasting; Facebook; India; TV rating system; TV show ratings; TV show viewers; Twitter messages; YouTube; advertising revenue rates; broadcasters; machine learning; social networks; Audience ratings; Micro blogging; Social media; web intelligence;
Conference_Titel :
Computational Intelligence and Information Technology, 2013. CIIT 2013. Third International Conference on
Conference_Location :
Mumbai
DOI :
10.1049/cp.2013.2586
