本研究以內容分析法比較共200首台灣與美國音樂錄影帶中的性內容以及愛情關係表現。研究結果顯示美國音樂錄影帶中的性內容普遍高於台灣的音樂錄影帶，不論是在歌詞表達或影像呈現中。但值得一提的是，臺灣2010年音樂錄影帶中相較於2000年音樂錄影帶中性內容增加的幅度大於美國2010年音樂錄影帶中相較於美國2000年因樂錄影帶中性內容的增加。而且愛情表現方面，台灣的歌曲不論是在2000年還是2010年都有超過80%以上的歌曲為愛情歌曲；美國歌曲則較為多元，從2000年到2010年，少了20%的愛情歌曲。 總體來說，此研究結果與先前研究的結果類似：台灣歌曲中的愛情表達多為無性的表達，而美國歌曲中則較易將愛情等同或化約成肉體慾望，且不論歌曲主題，性內容也較常表達在歌曲中。雖然台灣音樂錄影帶及歌曲中的性內容還尚未達到足以造成威脅的程度，由2010年的增加多於同時期美國的音樂錄影帶顯示未來研究更應重視這一塊因為大眾流行音樂錄影帶在年輕族群的生活中扮演了重要的角色。若其不當性內容持續增加，勢必會對年輕族群的性觀念及行為造成影響。 / This study examines 200 music videos from Taiwan the USA for their sexual content lyrical and visual presentation. The second focus is the illustration of themes in songs. Results of a content analysis shows that in terms of sexual content in music videos, the USA is still more sexual than Taiwan as a whole; however, the increase of sexual imagery in music videos is greater in Taiwan in 2010 compared to in 2000 than that from USA in 2010 compared to 2000. Within-country comparisons show that the increase in images is greater than that in lyrics from 2000 to 2010 in both countries. For themes in songs, in both years, over 80% of the songs analyzed from Taiwan are love theme songs. Songs from the USA are more varied, and from 2000 to 2010, there are 20% less love theme songs. On the whole, this study agrees with previous studies, which found that love manifested in lyrics is often an asexual kind of love in Taiwan, while in the USA physical desires are often outright portrayed, regardless of the song themes. Although the sexual level in music videos images have not yet reached an alarming level, researchers should, however, keep track of the sexual content because music videos do take up a part in forming a socializing environment, especially for the young people.

華語流行歌詞一直是語言、文學、音樂或是文化研究等相關科系赤手可熱的研究題目，內容包含作詞者、修辭分析、風格、用韻及語言表達等，然由於歌詞數量龐大，難以全部以人工分析。 近年來，資訊科技日新月異不斷地進步，隨著Big Data議題受到注目，Data Mining在近年來相當熱門，然而針對華語流行歌詞的巨量資料探勘與分析研究並不多。因此，本論文研究以程式來自動化分析歌詞的樣式與特性，包括詞彙頻率、詞彙相鄰關係分析、歌名分析、使用語系分析、舊曲新唱、歌詞風格自動分類、用韻及修辭等，而研究資料係透過網路擷取知名網站內容，包含魔鏡歌詞網 (Mojim.com)、臺北之音HitFM聯播網 (www.hitoradio.com) 及教育部重編國語辭典，透過分析規則及以Non-Trivial Repeating Pattern等方法，來完成分析及系統實作。 透過華語流行歌詞的大量分析，探勘及了解各種歌詞的風格與特性，將可了解各種歌詞、作詞者的風格與特色，進而應用在歌詞資料的管理與查詢。 此外，本研究將八萬多首歌詞的各種分析資料設置成網站，提供予學術研究使用，希冀此研究資料能使華語流行歌詞相關研究研究，進行更深入地探討。 / Chinese popular music lyrics has been a popular topic for researchers who major in languages and literature, music or culture. Related studies include of lyricists, rhetoric methods, styles, rhyme and language expression. However, all these studies were performed by manual analysis. It is difficult to analyze large amount of lyrics manually. With advances in computer technology, big data and data mining techniques have been widely used in different kinds of data. However, to the best of our knowledge, none have been done on pattern mining from big data of lyrics of Chinese popular music. Therefore, the objective of this thesis is to discover patterns from tremendous lyrics data based on data mining techniques. We use data downloaded from www.mojim.com, http://dict.revised.moe.edu.tw/cbdic/ and http://www.hitoradio.com (Hit FM). Data mining methods are employed to find lyrics’ patterns and features, including frequent words, word adjacency, analysis of hit songs' names, lyrics’ language studies, cover song research, automatic style prediction, rhyme and rhetoric patterns. With the analysis of tremendous lyrics and data, the developed approaches of this thesis will be helpful for discovering distinguishing styles of lyrics and lyricists.

科学研究費補助金 研究種目:基盤研究(C)(2) 課題番号:16520205 研究代表者:田所 光男 研究期間:2004-2005年度

本篇論文旨在研究中文流行音樂歌詞與歌曲之間情意的關聯性，並設計一個能推薦出符合歌曲情意的「以曲找詞歌詞推薦系統」。 流行音樂（Popular Music）在廣義上的定義為透過大眾媒體傳播、以大眾為閱聽對象的歌曲。其大眾化的特徵，使得流行音樂歌詞的主題多與日常生活息息相關且能清楚表達歌曲的情意，並以其所引起的共鳴性決定歌曲是否具出版的商業價值，人們也常常使用流行音樂歌曲來唱出屬於自己的故事、屬於自己的心聲。因此，本篇論文提出自動為流行音樂歌曲推薦符合歌曲情意的歌詞，讓舊有的歌曲搭配上新的歌詞，而當一首歌曲搭配了不同的歌詞就有了不同的故事，也帶給了原曲新的生命，達成一曲多詞的數位加值效果。 由文獻及專業音樂創作者的論述中，我們可以了解流行音樂詞曲有相關的搭配關係，其中又以詞曲情意的搭配關係最為重要，因此詞曲情意之間的關聯性為本研究問題的核心所在。透過大量分析市面上的流行歌曲，我們便可以從中看出詞曲之間情意搭配的線索。我們利用 LSA（Latent Semantic Analysis）演算法萃取出歌詞的情意特徵值，並比較其與語言學領域中隱喻融合理論的相似性，而在歌曲方面萃取出音高、調性、速度、節奏、和弦及音色等與等能展現歌曲情意的相關特徵值。然後利用了 CFA（Cross-Modal Factor Analysis）演算法來建立詞曲之間情意特徵值的關聯模型，最後我們便可以利用關聯模型來建立推薦系統，如此便完成了詞曲情意關聯為基礎的以曲找詞歌詞推薦系統。 實驗結果顯示，考慮詞曲情意特徵關聯所訓練出的關聯模型（CFA Feature Model）在以曲找詞推薦符合情意歌詞的前五名準確率平均達 60.1 %，前五十名也有 41.4 % 的準確率，比起僅考慮歌曲情意特徵（Audio Feature Model）以曲找詞推薦符合情意歌詞的前五名準確率 45.1% 及前五十名準確率28.6 % 準確率高，代表本研究所提出的詞曲情意關聯模型確實能有效推薦出符合歌曲情意的歌詞。我們也對本研究提出的詞曲情意關聯模型進行歌詞推薦結果的案例分析，我們輸入幾首學生創作的歌曲觀察詞曲情意關聯模型歌詞推薦結果，我們發現推薦出的流行音樂歌詞與學生創作的原詞在歌詞情意上非常類似，再次顯示本研究所提出的詞曲情意關聯模型確實能有效推薦出符合歌曲情意的歌詞，在詞曲創作上將能為創作者帶來靈感支援，幫助創作者詞曲創作。 / Nowadays lots of people use popular music to sing out their own story, and their own aspirations. In this thesis, we propose an approach to analyze the conception association between lyrics and music of Chinese popular music. And for applications, we design a lyrics recommendation system which can automatically recommend lyrics which is suitable to accompany with query music according to the affection and conception between lyrics and music. So, the old song with new lyrics, just like the song with different stories, brings the original song with new life. There are accompany association between lyrics and music, and the affection and conception association is most important among all. Therefore, analyze the conception association between lyrics and music is our goal. To do this, we can find out the association clues between lyrics and music from analyzing lots of popular music. For lyrics, we use LSA (Latent Semantic Analysis) algorithm to extract lyrics conception features. For music, we extracted the pitch, tonality, speed, rhythm, chords features which can show the music’s conception in the music. Then we use the CFA (Cross-Modal Factor Analysis) algorithm to analyze and learn the conception association between lyrics and music and establish the conception association model . Finally, we will be able to take advantage of the conception association model to establish the lyrics recommendation system. In the experimental results, when recommend the same conception lyrics to the query music, our proposed approach (CFA Feature Model) reaches accuracy of 60.1% on average in the top 5 recommended lyrics. Compared to control group approach (Audio Feature Model) only reaches accuracy of 45.1% on average in the top 5 recommended lyrics, our approach get better accuracy. We also presented some interesting lyrics recommendation results in case study. We upload some popular music created by students, and we found out that the affection and conception of the recommended lyrics are similar to the original song lyric which is created by the students. The experimental results show that the lyrics and music conception association model we proposed in this study does recommended lyrics suitable to the query music conception.