由於網際網路的蓬勃發展，串流音樂市場也隨之成長，使我們可以隨時取得
各種音樂。然而在眾多的歌曲中，要如何讓使用者以較低的搜尋成本獲取有興趣
的歌曲即成為一個困難的挑戰，音樂推薦系統即擔任了重要的角色。
本研究提出一個分析歌詞情緒與場景的英文歌曲推薦系統，利用ConceptNet
語義網絡與NRC 情緒詞庫分別抽取歌詞的情緒詞與場景詞。首先將歌曲視作多
種情緒主題的分布，利用隱含狄利克雷分布模型取得情緒主題機率分布作為歌曲
的情緒特徵。且為了理解歌詞中常有的心情轉折，提出以歌詞結構為基礎的標記
策略，將重要的副歌段落與其他段落分別訓練。同時利用TF-IDF 詞權重技術獲
得歌詞的場景特徵，再以多標籤分類器找到有相同場景的候選歌曲。最後計算情
緒特徵與場景特徵的相似性，找到目標歌曲的歌曲推薦清單。
經由實驗發現，本研究提出的歌曲推薦系統相較目前方法，在推薦不同歌曲
數下，系統合適度評價的精確率可提高6.1~20%，而在使用者系統偏好的實驗中
能獲得高於23%的偏好比例。

Due to the rapid growth of the Internet, the streaming music market has gradually
grown. This trend allows us to obtain plenty of songs at any time. Nevertheless, among
the huge amount, how to let users obtain the song they are interested in at a lower search
cost has become a difficult challenge. The music recommendation system has played
an important role in this issue.
In this thesis, we propose an English music recommendation system that analyzes
the emotion and scene context of lyrics. The ConceptNet semantic network and the
NRC emotional vocabulary are used to extract the emotion terms and scene terms of
the lyrics respectively. We regard song as a distribution of multiple emotional themes
and train Labeled LDA model to obtain the emotion distribution as the emotion feature.
Furthermore, in order to better understand the mood transitions often found in lyrics, a
label strategy based on the lyrics structure is proposed to train chorus and other
paragraphs separately. Meanwhile, a song uses TF-IDF word weighting technology to
obtain the scene feature of the lyric, and then make use of the multi-label classifier to
find candidate songs with the same scene. Finally, calculate the similarity between the
emotion features and the scene features, and find the song recommendation list of the
target song.
Through experiments, under different number of songs recommended, it is found
that our method can improve the accuracy of the system suitability evaluation by 6.1-
20% compared with the current method. As well as the experiment of user system
preference can be higher than 23%.

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