基於 Word2Vec 詞向量的網路情緒文和流行音樂媒合方法之研究

Many people share their feeling or story by writing emotional article on the Internet. They also attach a pop music in their text usually. This pop music has high relation with the meaning of the story. As the passed research show that people share their feeling through music all the time. This research use powerful computation power of computers to help people choose music when they are writing emotional article. We use neural network language model tool word2vec to build our recommender system. We also compare the performance with three baseline method including Boolean representation, TF-IDF, Okapi BM25. We use Chinese TOP-100 popular music monthly rank since 2005 to 2015 from Asia's largest music streaming provider KKBOX as our music dataset. The experiment result scored 0.3185 with mAP@5. According to our experiment result. 81% of users can get the correct music they want before five music recommended. It will be a usable system if we build a website or application.