A Survey of Opinion Mining for Texts - coli.uni-saarland.de

᭛ᴀᛣ㾕ᣪᥬ㓐䗄 ྮ໽ᯝ 1ˈ⿟Ꮰ᭛ 2ˈᕤ亲⥝ 2ˈ∝ᗱxРᗱ‫ܟ‬ᇨ⡍ 2,3ˈ⥟ⵓ 3 ˄1Ϟ⍋Ѹ䗮໻ᄺ䅵ㅫᴎ⾥ᄺϢᎹ⿟㋏ˈϞ⍋200240˗2ᖋ೑ҎᎹᱎ㛑ⷨおЁᖗˈᖋ೑㧼ᇨᏗ৩㚃 D-66123˗3ᖋ೑㧼ᇨᎲ໻ᄺ䅵ㅫ䇁㿔ᄺ㋏ˈᖋ೑㧼ᇨᏗ৩㚃D-66041˅ ᨬ㽕˖ᛣ㾕ᣪᥬᰃ䩜ᇍЏ㾖ᗻ᭛ᴀ㞾ࡼ㦋প᳝⫼ⱘᛣ㾕ֵᙃ੠ⶹ䆚ˈᅗᰃϔϾᮄ乪㗠Ϩकߚ䞡㽕ⱘⷨお䇒乬Ǆ䖭⾡ᡔᴃৃҹᑨ⫼Ѣ⦄ᅲ⫳⌏Ёⱘ䆌໮ᮍ䴶ˈབ⬉ᄤଚࡵǃଚϮᱎ㛑ǃֵᙃⲥ᥻ǃ⇥ᛣ䇗ᶹǃ⬉ᄤᄺ дǃ᡹ߞ㓪䕥ǃӕϮㅵ⧚ㄝǄᴀ᭛佪‫ܜ‬ᇍᛣ㾕ᣪᥬ䖯㸠њᅮНˈ✊ৢ䯤䗄њᛣ㾕ᣪᥬⷨおⱘⳂⱘˈ᥹ⴔҢ Џ乬ⱘ䆚߿ˈᛣ㾕ᣕ᳝㗙ⱘ䆚߿ˈ䰜䗄ⱘ䗝ᢽ੠ᚙᛳⱘߚᵤಯϾᮍ䴶ᇍᛣ㾕ᣪᥬⱘⷨお⦄⢊䖯㸠њ㓐䗄ˈ ᑊҟ㒡њ޴Ͼ៤ൟⱘ㋏㒳Ǆℸ໪ˈ៥Ӏ䩜ᇍ∝䇁ⱘᛣ㾕ᣪᥬ‫خ‬њ⡍߿ⱘߚᵤǄ᳔ৢᇍᭈϾ乚ඳⱘⷨお䖯㸠 њᘏ㒧Ǆ ݇䬂䆡˖ᛣ㾕ᣪᥬ˗Џ㾖ᗻ᭛ᴀ˗㓐䗄 Ё೒ߚ㉏ো˖TP319

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A Survey of Opinion Mining for Texts YAO Tian-fang1, CHENG Xi-wen2, XU Fei-yu2, Hans USZKOREIT2,3, WANG Rui3 (1. Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; 2. German Research Center for Artificial Intelligence, Saarbrücken D-66123, Germany; 3. Dept. of Computational Linguistics, Saarland University, Saarbrücken D-66041, Germany) Abstract: Opinion Mining is a novel and important research topic, aiming to automatically acquire useful opinioned information and knowledge in subjective texts. This technique has wide and many real-world applications, such as e-commerce, business-intelligence, information monitoring, public-opinion poll, e-learning, newspaper and publication compilation, business management, etc. In this paper, we give a definition for opinion mining and then describe the motivation of this research. Afterwards, we present a survey on the state-of-the-art of opinion mining on top of four subtasks: topic extraction, holder identification, claim extraction and sentiment analysis, followed by an overview of several existing systems. In addition, specific analysis on Chinese Opinion Mining is performed. Finally, we provide the summarization of opinion mining research. Key words: opinion mining; subjective text; survey

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㱑✊೑䰙೑‫ⱘݙ‬ϔѯⷨお㗙Ꮖ㒣ᓔሩњᇍᛣ㾕ᣪᥬᡔᴃⱘⷨおˈгѻ⫳њϔѯᑨ⫼㋏㒳 ˄㾕ϟϔ㡖Āᛣ㾕ᣪᥬⷨお⦄⢊ā˅ ǄԚᅗ䖬ᰃ䴶Јϔѯᇮ᳾㾷‫ⱘއ‬䯂乬ˈབᛣ㾕ᣪᥬᮍ⊩ⱘ ㊒ᑺ੠剕Ầᗻϡ⧚ᛇǃ䱤ᓣЏ乬˄䰜䗄Ёϡࣙ৿‫݋‬ԧ㸼⼎Џ乬ⱘ䆡∛៪ⷁ䇁˅ⱘ䆚߿ҹঞ㒚乫㉦ᑺⱘЏ乬੠ᚙᛳ݇㋏䆚߿˄བ໮Џ乬੠໮ᚙᛳⱘᇍᑨ݇㋏˅ㄝǄ ՟ ˖Āৢ໛ㆅ≵᳝‫ݙ‬ᓔ㺙㕂ˈ៥㾝ᕫᕜϡᅝܼˈгᕜϡ⠑ʽ ā ೼՟ ⱘহᄤЁ㄀ϔϾᄤহᰃᅶ㾖হˈ㄀Ѡǃϝᄤহᰃࣙ৿ᛣ㾕ⱘ䰜䗄ǄԚᰃ䖭ϸϾ䰜䗄Ё㔎УᰒᓣЏ乬˄䰜䗄Ёࣙ৿‫݋‬ԧ㸼⼎Џ乬ⱘ䆡∛៪ⷁ䇁˅Ǆℸ໪ˈᅗӀⱘЏ乬ϡᰃĀ‫ݙ‬ ᓔ㺙㕂āˈ㗠ᰃĀৢ໛ㆅāǄᅗᇍᑨњϸϾᚙᛳᦣ䗄乍ˈϔϾᰃĀᕜϡᅝܼāˈ঺ϔϾᰃĀᕜ ϡ⠑āǄ⊼ᛣ䖭ϸϾᚙᛳᦣ䗄乍Ёࣙ৿᳝৺ᅮ䆡Ǆ᠔ҹˈᅗӀⱘ䇁Нؒ৥ϢĀᅝܼā੠Ā⠑ā Ⳍডˈᰃ䌀НⱘǄ‫ࡴݡ‬Ϟׂ佄䆡Āᕜāˈࡴᔎњ䌀Н䇁Нؒ৥ⱘᔎᑺǄ঺໪ˈҢ㄀ϝহᄤহ ЁⱘĀ⠑āᄫˈৃҹⶹ䘧ᚙᛳᦣ䗄乍ϡԚ⍝ঞк䴶㸼䖒ᔶᓣˈг⍝ঞষ䇁㸼䖒ᔶᓣǄ 㓐Ϟ᠔䗄ˈᛣ㾕ᣪᥬᰃϔ⾡ᕜ᳝ࠡ䗨ⱘᮄ乪ⱘ䇁㿔ᡔᴃǄԚᰃˈᅗৠᯊг䴶Јⴔ䆌໮ᇮ ᳾㾷‫ⱘއ‬䯂乬੠ᣥ៬Ǆ಴ℸˈᇍѢᛣ㾕ᣪᥬᮍ⊩੠ᡔᴃⱘⷨおˈϡԚ‫݋‬᳝⧚䆎ᛣНˈ㗠Ϩ‫݋‬ ᳝ᅲ⫼ӋؐǄ

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೼䖭㡖Ёˈ៥Ӏ佪‫ܜ‬ҢЏ乬ⱘ䆚߿ǃᣕ᳝㗙ⱘ䆚߿ǃ䰜䗄ⱘ䗝ᢽ੠ᚙᛳⱘߚᵤ䖭޴Ͼᮍ 䴶ҟ㒡೑䰙ϞᇍѢᛣ㾕ᣪᥬⱘⳌ݇ᮍ⊩੠ᡔᴃⱘⷨお⦄⢊ˈ✊ৢҟ㒡њ޴Ͼ៤❳ⱘᛣ㾕ᣪᥬ ᑨ⫼㋏㒳Ǆ᳔ৢᇚҟ㒡೑‫ݙ‬ᛣ㾕ᣪᥬᮍ⊩੠ᡔᴃⱘⷨお⦄⢊ˈЏ㽕ᰃ䩜ᇍ∝䇁ⱘ⡍⚍᥶䅼ᛣ 㾕ᣪᥬⱘᮍ⊩੠ᡔᴃǄ

Џ乬ⱘ䆚߿ Ўњ⏙Ἦഄњ㾷䆚߿Џ乬ⱘӏࡵˈ䇋খ㗗བϟ՟ᄤ˖

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ᛣ㾕ᣕ᳝㗙ⱘ䆚߿

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䰜䗄ⱘ䗝ᢽ

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ǃ䋱৊ᮃ ǃㄯ䗝˄:LQQRZ˅ ㄝǄℸ໪ˈ䰸њ㒣‫ⱘ݌‬ᴎ఼ᄺдㅫ⊩ˈ3DQJ ੠ /HH 䞛 ⫼෎Ѣᇏᡒ᭛ḷⱘ᳔ᇣ೒ࡆ˄PLQLPXPFXWVLQJUDSKV˅ⱘᮍ⊩ᴹᇏᡒ᭛ḷЁⱘЏ㾖ᗻ䚼ߚ >@ ˈे䆚߿᭛ḷЁ䇁হϢᏆⶹЏ㾖䇁হⱘᇍᑨ݇㋏ˈҢ㗠䖒ࠄᇍЏᅶ㾖䇁হߚ㉏ⱘⳂⱘǄ䖭 Ͼᮍ⊩㛑໳㹿៤ࡳഄᑨ⫼Ѣ䇁হሖ੠᭛ḷሖⱘЏ㾖ᗻߚᵤǄ ㄀Ѡˈ㒚乫㉦ᑺ䰜䗄ⴔⴐѢ᳈⡍ᅮⱘ㣗ೈˈҢ㗠ৃҹᢑপЏ乬੠䰜䗄П䯈㋻ᆚⱘ݇㋏Ǆ >@ བᅮНࣙ৿ᔶᆍ䆡៪ࡃ䆡ⱘৡ䆡ⷁ䇁԰Ў䰜䗄 Ǆ೼՟ Ёˈⷁ䇁՟བĀᣕ㓁᳔⬙䫔ⱘᖋ೑ ≑䔺ā੠ᄤহ՟བĀԚࠍ䔺䏣ᵓ໾䕃āǃĀᅗⱘ‫ݙ‬䚼䴲ᐌ㊒㟈āᰃ㸼䖒ᛣ㾕ⱘ㒚乫㉦ᑺ䰜䗄Ǆ >@ :LOVRQ ㄝҎᓔ߯њ೼ⷁ䇁ሖ䖯㸠Џ㾖ᗻߚᵤⱘⷨおᎹ԰ ǄҪӀḍ᥂ Ͼ⏋ড়⡍ᕕ䆁㒗њ ϔϾ %RRV7H[WHU ߚ㉏఼Ǆ䖭ѯ⏋ড়⡍ᕕ㹿ߚ៤ѨϾ㉏߿˖䆡∛⡍ᕕ˄՟བˈϞϟ᭛ǃॳྟ ᵕᗻ˅ǃׂ佄⡍ᕕ˄ᔶᆍ䆡ᓔྟǃׂ佄ᔎⱘЏ㾖ᗻᣛ⼎ヺ˅ǃহᄤ⡍ᕕ˄՟བˈᔧࠡহЁᔎⱘ Џ㾖ᗻᣛ⼎ヺ˅ǃ㒧ᵘ⡍ᕕ˄՟བˈ㹿ࡼᗕ˅੠᭛ḷ⡍ᕕ˄Џ乬˅Ǆᅲ偠㒧ᵰ˖䖭ϾѨ⡍ᕕ䲚 ড়ⱘߚ㉏఼೼㒚乫㉦ᑺЏ㾖ᗻߚᵤӏࡵЁᅠ៤ഄᕜདǄ >@ ㄀ϝˈ䖬᳝ϔѯᮍ⊩ Փ⫼⡍ᅮ᠟Ꮉ㓪䕥ⱘ䇁㿔῵ᓣᴹҢ䆘䆎Ёᢑপ䰜䗄Ǆ՟བˈᇍ Ѣ䇁হĀᮄℒᓣᇚ਌ᓩ䆌໮䅸Ўᖋ೑ࠊ䗴њ᳔ད≑䔺ⱘ㕢೑ҎǄāˈ䞛⫼䇁㿔῵ᓣǇ਌ᓩˈЏ 䇁˄Џ乬˅ˈᆒ䇁˄ᛣ㾕ᣕ᳝㗙˅ǈᴹᢑপ䰜䗄Ǆ䖭Ͼ䇁㿔῵ᓣ㸼⼎԰Ўᆒ䇁ⱘᛣ㾕ᣕ᳝㗙 ᇍѢ԰ЎЏ䇁ⱘЏ乬᳝ϔϾ㻦Нⱘᛣ㾕Ǆ✊ৢˈ㹿ᢑপⱘ䰜䗄ህᰃǇ਌ᓩˈ˄ᮄℒᓣ˅ˈ˄㕢 ೑Ҏ˅ǈˈᅗᣛߎњ㕢೑Ҏ䩜ᇍЏ乬Āᮄℒᓣā᳝ϔϾ㻦Нⱘᛣ㾕Ǆ䖭⾡䰜䗄ⱘ㣗ೈᰃ䴲ᐌϹ Ḑⱘᑊ䖒ࠄњ䕗催ⱘ㊒ᑺǄԚᰃˈᅗ䲒ҹᬍ䖯῵ᓣⱘ㽚Ⲫ䴶੠ৃ䗖ᑨᗻǄ

ᚙᛳⱘߚᵤ >@

ᖗ⧚ᄺⷨお থ⦄ˈ䆡∛੠Ҏ㉏ᚙᛳП䯈ⱘ݇㋏ᰃৃᑺ䞣ⱘˈ⣀ゟⱘ䆡∛៪ⷁ䇁ⱘ䇁Н ؒ৥ᇍѢӴ䖒Ҏ㉏ᚙᛳᰃ䞡㽕ⱘǄ䇋ⳟҹϟⱘ՟ᄤ˖ ՟ ˖Ā⇨‫׭‬ᅰҎⱘᮙ␌㚰ഄ੠ᓩҎܹ㚰ⱘቅቅ∈∈Ǆā ೼Ϟ՟Ёˈᔶᆍ䆡Ā⇨‫׭‬ᅰҎā੠ĀᓩҎܹ㚰āߚ߿㸼䖒њᇍЏ乬Āᮙ␌㚰ഄā੠Āቅ ቅ∈∈ā㻦НⱘᚙᛳǄⳈ㾖ഄⳟˈᇍ↣ϔϾ䰜䗄ˈᘏⱘ䇁Нؒ৥དണ䞣ؐ㹿⫼ᴹ㸼⼎ᛣ㾕 ᣕ᳝㗙ⱘᚙᛳǄ⦄Ҟⱘⷨお䞡⚍ᰃ䇁Нؒ৥ⱘ㞾ࡼ㦋পǄ䩜ᇍ䇁㿔ⱘ⡍㡆ˈ᳝ϸϾ⦄䈵㹿ᑓ >@ ⊯ഄ⫼ᴹ㦋প䆡∛੠ⷁ䇁ⱘ䇁Нؒ৥˖㄀ϔˈⳌৠؒ৥ⱘᚙᛳᴃ䇁㒣ᐌৠᯊߎ⦄ ˗㄀Ѡˈ >@ Ⳍডؒ৥ⱘᚙᛳᴃ䇁ϔ㠀ϡϔ䍋ߎ⦄ Ǆḍ᥂䖭ϸϾ⦄䈵ˈৃҹҙՓ⫼ᕜᇥⱘ⾡ᄤ˄՟བˈ ĀH[FHOOHQWāᰃ㻦Нⱘˈ ĀSRRUāᰃ䌀Нⱘ˅ҢϔϾ໻ൟ䇁᭭ᑧЁ˄՟བˈѦ㘨㔥˅㦋ᕫ䆡 ∛੠ⷁ䇁ⱘᚙᛳؒ৥ǄЎњথ⦄᳈໮ⱘᚙᛳ䆡∛੠ⷁ䇁ˈ㦋ᕫᚙᛳؒ৥ⱘ䆡੠ⷁ䇁㛑໳㹿ࡴ >@ ܹࠄ⾡ᄤ䲚Ё䖯㸠䗁ҷᇏᡒ Ǆ䰸њ䇁㿔ⱘ⡍⚍ˈᏆᄬ೼ⱘ䌘⑤᠔䍋ⱘ԰⫼гकߚᰒ㨫Ǆ >@ ↨བˈ:RUG1HW Ё䆡∛П䯈ⱘ䎱⾏ৃҹᧁ⼎ᚙᛳؒ৥ⱘ݇㋏ Ǆℸ໪ˈ݊Ёᔶᆍ䆡ⱘৠН䆡 >@ ੠ডН䆡䲚гৃҹ㹿⫼ᴹ乘⌟ᚙᛳؒ৥ Ǆ ✊㗠ˈᚙᛳ㹿Ӵ䗦ᯊϡҙ䗮䖛ऩϔ䆡∛៪ⷁ䇁ˈ㗠Ϩгৃ㛑䗮䖛ᅗӀⱘ㒘ড়Ǆḍ᥂ϟ䴶 ⱘ՟ᄤˈेՓᔶᆍ䆡Ā⒵ᛣāӴ䗦㻦НⱘᵕᗻˈԚᰃᭈϾ䰜䗄㸼䖒њⳌডⱘᵕᗻǄ ՟ ˖Ā៥ӀᇍѢଂৢ᳡ࡵϡᰃᕜ⒵ᛣǄā 䖭Ͼ䰜䗄ⱘᚙᛳ⬅䆡∛ⱘ㒘ড়Āϡᰃᕜ⒵ᛣā᠔㸼䖒Ǆ䆡∛ĀϡᰃāӴ䗦њ䆡∛Ā⒵ᛣā ⱘⳌডᵕᗻǄ䖭ḋˈ‫ڣ‬Āϡᰃā䖭ḋⱘ䆡∛ሲѢ䜡Ӌ⿏ࡼᣛ⼎ヺ˄9DOHQFH6KLIWHU,QGLFDWRUˈ 96,˅ǄेՓ䜡Ӌ⿏ࡼᣛ⼎ヺϡ‫݋‬᳝䇁Нؒ৥ˈᅗӀг㛑Ӵ䗦ᇍᑨᚙᛳؒ৥ᘏⱘ䞣ؐⱘⳌডᵕ ᗻǄ಴ℸˈ៥Ӏᑨ䆹㗗㰥䍙ߎ䇁Нؒ৥᳈໮ⱘ䰤ࠊǄⳂࠡˈ䖯ϔℹⱘⷨおᎹ԰ϡҙϧ⊼ѢϾ

߿䆡∛੠ⷁ䇁ˈг䳔㽕ϧ⊼Ѣ䰜䗄Ё໮Ͼ‫ܗ‬㋴П䯈ⱘⳌѦ԰⫼Ǆ↨བˈϔѯᮍ⊩䞛⫼њ᠟Ꮉ >@ ᵘ䗴䇁㿔῵ᓣ៪䌟ќ䰜䗄ᵕᗻⱘ䰤ࠊ Ǆ՟བˈ೼՟ Ёˈ䇁㿔῵ᓣǇᇍѢ 32%- ϡᰃ ᕜ⒵ᛣǈ㛑㹿ᆍᯧഄ⫼ᴹऎ߿Ꮖ฿ܹῑǇ32%- ǈЁⱘЏ乬Āଂৢ᳡ࡵāᰃ䌀НⱘǄ䖭Ͼ ᮍ⊩ѻ⫳њⳌᔧ催ⱘ㊒ᑺǄԚᰃˈᅗ㽕∖䆌໮᠟ᎹⱘᎹ԰㗠Ϩ䇁㿔⦄䈵ⱘ㽚Ⲫ䴶ϡᑓǄ >@ ঺ϔϾᮍ⊩ϧ⊼ѢϞϟ᭛৘‫ܗ‬㋴П䯈ⱘⳌѦ԰⫼ Ǆ䖭Ͼ䖛⿟佪‫ܜ‬㦋প䆡∛ⱘ䇁Нؒ ৥ˈ✊ৢḍ᥂᠟Ꮉ㓪䕥ⱘϔѯ䰤ࠊ˄՟བˈᑊ߫݇㋏䖲᥹䆡䖲᥹Ⳍৠ䇁Нؒ৥ⱘ䆡˅㒭‫ܗ‬㒘 ǇZRUGǈǃ ǇZRUGˈWRSLFǈ੠ǇZRUGˈWRSLFˈVHQWHQFHǈҹ䗁ҷᮍᓣᣛ⌒ᵕᗻǄ՟བˈ೼՟ Ёˈ Ā⒵ᛣā䗮䖛হ⊩ձ䌪㾘߭㹿䗝ᢽЎᚙᛳ䆡∛ˈ✊ৢ䗮䖛 30, ㅫ⊩ˈᚙᛳ䆡∛Ā⒵ᛣā ᓔྟᯊ㹿ᣛ⌒ϔϾᵕᗻǄḍ᥂䰤ࠊᴵӊˈᑊ߫݇㋏䖲᥹䆡Ā੠ā㘨㋏‫݋‬᳝ⳌৠᵕᗻⱘϸϾᔶ ᆍ䆡ˈ᠔᳝ⱘৠН䆡‫݋‬᳝ⳌৠⱘᵕᗻǄ䖭ḋˈѻ⫳њ㄀ϔϾ‫ܗ‬㒘˄D˅Ǉ⒵ᛣǈˈᅗ‫݋‬᳝ℷⱘ ᵕᗻˈ᥹ⴔˈḍ᥂䖭Ͼㅫ⊩ˈ㄀ѠϾ‫ܗ‬㒘˄E˅ ǇDˈଂৢ᳡ࡵǈⱘᵕᗻㄝѢ㄀ϔϾ‫ܗ‬㒘ⱘᵕ ᗻˈᅗг㛑໳ḍ᥂Ϟϟ᭛ᣛ⼎ヺⱘ䰤ࠊ㹿ᬍব˄՟བˈᑊ߫៪䕀ᡬ݇㋏䖲᥹䆡˅ǄⳌԐⱘㄪ ⬹г㹿ᑨ⫼ࠄ㄀ϝϾ‫ܗ‬㒘˄F˅DE៥ӀᇍѢଂৢ᳡ࡵϡᰃᕜ⒵ᛣ!Ǆ⬅Ѣ䆡∛Āϡᰃā ᰃ 96,ˈ㄀ϝϾ‫ܗ‬㒘ⱘᵕᗻϢ㄀ѠϾ‫ܗ‬㒘ⳌডǄ䖭Ͼ䗁ҷ䖛⿟Ⳉࠄ೼䇁᭭ᑧЁ‫ܗ‬㒘ⱘᵕᗻϡ 㛑㹿ᬍবЎℶǄ䖭⾡ᮍ⊩೼໻䞣᠟Ꮉ䰤ࠊᑆ乘ϟˈ㛑໳䖒ࠄ䕗催ⱘ‫⥛⹂ޚ‬Ǆ Ϟ䗄ⱘ䇁㿔῵ᓣ੠䰤ࠊ䛑䳔㽕໻䞣ҎᎹֵᙃⱘᑆ乘ˈ಴ℸЎњᦤ催ᡔᴃⱘ乚ඳᅲ⫼ᗻˈ ঺໪ⱘⷨおᡞ䖭ѯֵᙃ䛑԰Ў⡍ᕕএ䆁㒗ᚙᛳ㒳䅵῵ൟǄ೼䖭ᮍ䴶ˈ៥ӀЏ㽕ҟ㒡ᇍѢϡৠ >@ 䰜䗄㣗ೈ᠔䗝পⱘ⡍ᕕǄ᭛ḷሖ῵ൟ䲚៤њ 1 ‫ܗ‬⡍ᕕˈ䖒ࠄњ䕗催ⱘᗻ㛑 ǄԚᰃˈ䖭ѯ >@ ῵ൟ೼হᄤሖߚᵤᬜᵰ䕗Ꮒ ˈᅗӀ䳔㽕ড়ᑊ䩜ᇍহᄤሖߚ㉏ⱘ䰘ࡴ⡍ᕕǄ䰸њ 1 ‫ܗ‬῵ൟП >@ ໪ˈҢ㒘៤៪ձ䌪㒧ᵘᢑপⱘᄤІᬍ୘њহᄤሖ῵ൟⱘᗻ㛑 Ǆ⬅Ѣ೼হᄤЁⱘ⿔⭣ֵᙃˈ >@ ҪӀ䅸Ў䲚៤᳈໮ⱘ⡍ᕕᰃᖙ㽕ⱘ Ǆ >@ ᳔䖥ˈϔѯⷨおᎹ԰Ꮖ㒣ᓔྟ᥶㋶᳈㒚乫㉦ᑺⱘᚙᛳߚᵤ ǄҪӀՓ⫼ǃᬊ䲚੠⏋ড়᠔ ᳝ᦤࠄⱘ⡍ᕕ˄՟བׂ佄䇁ⱘᵕᗻǃ䗮ᐌⱘ 96, 䆡∛‫ڣ‬ĀOLWWOHWKUHDWā ǃ৺ᅮᵕᗻ⿏ࡼヺ ‫ڣ‬ĀODFNRIXQGHUVWDQGLQJā˅ᴹᣛ⌒ᵕᗻ㒭ⷁ䇁੠ᄤহǄᅲ偠䆕ᅲњ䆌໮䇁㿔ֵᙃⱘড়ᑊ >@ ᰒ㨫ഄᬍ䖯њ㒚乫㉦ᑺⱘᚙᛳߚᵤⱘᗻ㛑Ǆ&KHQJ ⷨおњ㒚乫㉦ᑺⱘᚙᛳߚᵤ Ǆཌྷ䞛⫼ᴎ ఼ᄺдᮍ⊩Ңऩϔ乚ඳ䇁᭭ᑧЁᄺд㦋প⣀ゟ䆡∛੠ 96, ⱘ䇁Нؒ৥Ǆḍ᥂Џ乬ⱘᴀԧὖᗉ ੠䇁Нؒ৥Փ⫼ਃথᓣ㾘߭䗝ᢽ䰜䗄Ǆ೼ℸ෎⸔ϞˈՓ⫼㒳ϔⱘ㸼⼎䲚៤‫݋‬᳝ᚙᛳⶹ䆚ⱘ䇁㿔⡍ᕕˈ✊ৢ䞛⫼ᴈ㋴䋱৊ᮃߚ㉏఼ߚ㉏ᚙᛳᵕᗻǄ

ᛣ㾕ᣪᥬᑨ⫼㋏㒳

ᛣ㾕ᣪᥬᡔᴃ԰Ўϔ⾡ᮄ乪ⱘ䇁㿔ᡔᴃϡҙৃҹ䖤⫼Ѣ㞾✊䇁㿔໘⧚㋏㒳Ёˈ䖬ৃҹᑨ ⫼Ѣ⦄ᅲ⫳⌏ЁǄ՟བ˖ 'DYH ㄝҎⷨおᑊᓔথⱘ 5HYLHZ6HHU ᰃϪ⬠Ϟ㄀ϔϾᚙᛳߚᵤᎹ‫݋‬੠㄀ϔϾ䩜ᇍ㒭ᅮѻ >@ ક䆘䆎ऎ߿݊㻦䌀ᗻⱘ㋏㒳 Ǆ*DPRQ ㄝҎⷨおᑊᓔথⱘ 3XOVH ㋏㒳ৃҹ㞾ࡼᣪᥬ㔥Ϟ⫼᠋ >@ ᠔Ϟ䕑ⱘ㞾⬅᭛ᴀЁ᳝݇≑䔺䆘ӋЁⱘ䌀㻦ֵᙃ੠ᔎᔅ⿟ᑺ Ǆ/LX ㄝҎⷨおᑊᓔথⱘ >@ 2SLQLRQ2EVHUYHU ㋏㒳ৃҹ໘⧚㔥Ϟ೼㒓乒ᅶѻક䆘Ӌ ˈᇍ⍝ঞѻક˄བ⬉ᄤ✻Ⳍᴎ˅ ৘⾡⡍ᕕⱘӬ㔎⚍䖯㸠㒳䅵ˈᑊ䞛⫼ৃ㾚࣪ᮍᓣᇍ㢹ᑆ⾡ѻકⱘ⡍ᕕⱘ㓐ড়䋼䞣䖯㸠↨䕗Ǆ >@ @ 㾕⑤ǃⳈ᥹ⱘЏ㾖ᗻ㸼䖒ǃ䇈䆱џӊ˄VSHHFKHYHQW˅ǃᚙᛳㄝ˅ⱘ㋏㒳 Ǆ ᳔ৢˈЎњ᳈⏙Ἦഄ↨䕗৘Ͼᑨ⫼㋏㒳᠔ᣪᥬⱘᛣ㾕‫ܗ‬㋴ǃᚙᛳߚᵤ㉏ൟҹঞᚙᛳⶹ䆚 Փ⫼ᚙ‫߫ˈމ‬㸼བϟ˖

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৘Ͼᑨ⫼㋏㒳᠔ᣪᥬⱘᛣ㾕‫ܗ‬㋴ǃᚙᛳߚᵤ㉏ൟҹঞᚙᛳⶹ䆚Փ⫼ᚙ‫↨ⱘމ‬䕗 ᚙᛳߚᵤ

ᚙᛳⶹ䆚

㉏ൟ

Փ⫼ᚙ‫މ‬

䰜䗄ˈᚙᛳ!

㉫乫㉦

᳾Փ⫼

3XOVH

䰜䗄ˈЏ乬ᴃ䇁ˈᚙᛳ!

㉫乫㉦

Փ⫼

2SLQLRQ2EVHUYHU

䰜䗄ˈЏ乬ᴃ䇁ˈᚙᛳ!

㉫乫㉦

Փ⫼

:HE)RXQWDLQ

ᛣ㾕ᣕ᳝㗙ˈ䰜䗄ˈЏ乬ᴃ䇁ˈᚙᛳ!

⡍ᅮ乫㉦

Փ⫼

2SLQLRQ)LQGHU

ᛣ㾕ᣕ᳝㗙ˈ䰜䗄ˈЏ乬ᴃ䇁ˈᚙᛳ!

㒚乫㉦

Փ⫼

ᑨ⫼㋏㒳ৡ⿄

᠔ᣪᥬⱘᛣ㾕‫ܗ‬㋴

5HYLHZ6HHU

∝䇁ᛣ㾕ᣪᥬⷨお⦄⢊

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䆡ҹࡴᔎᇍ᭛ᴀ㻦䌀Нᔎᑺⱘ䆚߿Ǆ >@ ೼ᑨ⫼㋏㒳ᮍ䴶ˈྮ໽ᯝㄝҎⷨおᑊᓔথњ⫼Ѣ∝䇁≑䔺䆎യⱘᛣ㾕ᣪᥬ㋏㒳 Ǆ䆹㋏㒳ৃ೼⬉ᄤ݀ਞᵓǃ䮼᠋㔥キⱘ৘໻䆎യϞᣪᥬᑊϨὖᣀᛣ㾕ᣕ᳝㗙ᇍ৘⾡≑䔺ક⠠ⱘϡৠ ᗻ㛑ᣛᷛⱘ䆘䆎੠ᛣ㾕ˈᑊϨ߸ᮁ䖭ѯᛣ㾕ⱘ㻦䌀ᗻҹঞᔎᑺǄ᳔ৢˈ䗮䖛ᇍ᭛ᴀ໘⧚ⱘ㓐 ড়㒳䅵ˈᑊ㒭ߎৃ㾚࣪ⱘ㒧ᵰǄ

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Ϟ䴶ҟ㒡њ೑‫ݙ‬໪೼ᛣ㾕ᣪᥬᮍ⊩ǃᡔᴃ੠ᑨ⫼ⷨおЁ᠔পᕫⱘ៤ᵰǄ೼䖭ѯⷨおЁˈ Џ㽕ᰃೈ㒩ⴔಯϾᄤӏࡵሩᓔⱘǄ݊ЁᇍЏ乬ᢑপ੠ᚙᛳߚᵤⱘⷨおᰒᕫᇸЎ䞡㽕ˈ಴Ўᅗ ӀҹঞᅗӀП䯈ⱘ݇㋏ᰃᛣ㾕ЁⱘḌᖗ‫ܗ‬㋴੠݇㋏Ǆ᠔ҹˈ೼䖭ᮍ䴶ⱘⷨおᎹ԰↨䕗䲚ЁǄ 㱑✊೼೑‫ݙ‬໪᠔䖯㸠ⱘᛣ㾕ᣪᥬᮍ⊩ǃᡔᴃ੠ᑨ⫼ⷨおЁᏆপᕫњⳌᔧⱘ䖯ሩˈԚ䖬ᄬ ೼ⴔϔѯϡ䎇П໘ˈЏ㽕ᰃ˖ (1) ⬅Ѣᛣ㾕ൟЏ㾖ᗻ᭛ᴀ೼к‫ݭ‬ᯊ䱣ᛣᗻ䕗໻ˈ䘷䆡䗴হ෎ᴀ≵᳝㑺ᴳǄᇍᛣ㾕ᣪᥬ 䖯㸠ⷨおˈ佪‫ܜ‬ᑨҢ䇁㿔ᄺⱘ㾦ᑺᇍ䖭⾡Џ㾖ᗻⱘ᭛ᴀ䖯㸠ⷨおˈࣙᣀ䇁᭭ᬊ䲚ǃ ߚᵤ䇁㿔㾘ᕟǃⷨおᷛ⊼㾘㣗੠ᮍ⊩ㄝǄԚ೼೑‫ݙ‬໪ⱘⷨおЁˈ䖭ᮍ䴶ⱘ෎⸔ⷨお䖬‫خ‬ᕫᕜᇥǄ (2) Ⳃࠡ໻䚼ߚⱘᛣ㾕ᣪᥬᮍ⊩䖬ᰃ㉫乫㉦ᑺⱘǄ䖭⾡ᮍ⊩᠔䗴៤ⱘ䯂乬᳝˖Џ乬ᴃ䇁䆚߿੠ᚙᛳߚᵤㄝⱘ㊒ᑺ੠剕ẦᗻϡབҎᛣ˗Џ乬ᴃ䇁㒧ᵘ㒘㒛ϡ⏙Ἦ˗䱤ᓣЏ乬 ⱘ䆚߿ⷨお⃴㔎˗᠔⍝ঞⱘЏ乬੠ᚙᛳП䯈݇㋏Џ㽕ᰃㅔऩ݇㋏˄བϔᇍϔⱘ݇㋏˅ ㄝǄ䖭⾡㉫乫㉦ᑺⱘᛣ㾕ᣪᥬ㒧ᵰϡ㛑Ⳉ᥹⒵䎇⫼᠋㒚乫㉦ᑺⱘᛣ㾕ᣪᥬ䳔∖ˈབ ݇Ѣ໮Џ乬ⱘᚙᛳ݇㋏ҹঞⳌᑨⱘ䇁Нؒ৥੠ᔎᑺㄝǄ (3) ⬅Ѣⷨおⱘ⏅ᑺ᳝䰤ˈⳂࠡᛣ㾕ᣪᥬⱘϔѯᮍ⊩੠ᡔᴃ䖬ᕜ䲒Ϣ݊ᅗ᳝݇ⱘᮍ⊩੠ ᡔᴃⳌ↨䕗ˈབӴ㒳ⱘ݇㋏ᢑপӏࡵПϔ˖џӊᢑপǄ (4) ᇍ∝䇁᭛ᴀᛣ㾕ᣪᥬᮍ⊩ⱘⷨお䍋ℹѢ䖥ϸᑈˈ᠔‫ⷨⱘخ‬おᎹ԰䖬ϡ໮Ǆ䖬䴶Ј㔎 У∝䇁Џ㾖ᗻ᭛ᴀ䇁᭭ᑧǃ㔎У䇁᭭ᷛ⊼㾘㣗ǃ㔎У∝䇁᭛ᴀᛣ㾕ᣪᥬᮍ⊩ⱘ㋏㒳 ᗻⷨおǃ∝䇁ᛣ㾕ᣪᥬଚ⫼㋏㒳г᳾㾕᡹䘧ㄝǄ >@ ಴ℸˈ㒧ড়៥ӀҢџᛣ㾕ᣪᥬⷨおᎹ԰ⱘ㒣ग़ ˈ⏅ᛳϞ䗄䯂乬ⱘᄬ೼Փᕫᛣ㾕ᣪ ᥬᮍ⊩੠ᡔᴃϢᅲ䰙ᑨ⫼䖬Ⳍ䎱䕗䖰ǄЎњ㛑໳ᓹ㸹Ϟ䗄ᦤࠄⱘⷨおЁⱘϡ䎇ᑊ㓽ⷁ೑‫ݙ‬Ϣ ೑໪೼䖭ᮍ䴶ⷨおⱘᏂ䎱ˈ⡍߿ᰃ೼ᵘᓎ∝䇁Џ㾖ᗻ᭛ᴀ䇁᭭ᑧǃᓎゟ∝䇁᭛ᴀᛣ㾕ᣪᥬ䅵 ㅫ῵ൟǃᅲ⦄໘⧚໻㾘῵∝䇁ⳳᅲ᭛ᴀⱘᑨ⫼㋏㒳ㄝᮍ䴶ࡴᔎⷨおˈᮽߎ៤ᵰˈҹ䗖ᑨ៥Ӏ ೑ᆊ㒣⌢੠⼒Ӯᇍ䖭ᮍ䴶᮹Ⲟ๲䭓ⱘᑨ⫼䳔∖ˈ᮴⭥ᰃकߚ䞡㽕੠ᖙ㽕ⱘǄ

খ㗗᭛⤂˖ [1] S.-M. Kim and E. Hovy. Determining the Sentiment of Opinions [A]. In: Proceedings of COLING-04, the Conference on Computational Linguistics (COLING-2004) [C]. Geneva, Switzerland: 2004, 1367-1373. [2] J Yi, T. Nasukawa, R. Bunescu, and W. Niblack. Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques [A]. In: Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM-2003) [C]. Melbourne, Florida: 2003, 427-434. [3] M Hu and B. Liu. Mining Opinion Features in Customer Reviews [A]. In: Proceedings of Nineteeth National Conference on Artificial Intellgience (AAAI-2004) [C]. San Jose, USA: 2004. [4] A.-M. Popescu and O. Etzioni. Extracting Product Features and Opinions from Reviews [A]. In: Proceedings of HLT-EMNLP-05, the Human Language Technology Conference/Conference on Empirical Methods in Natural Language Processing [C]. Vancouver, Canada: 2005, 339-346.

[5] X. Cheng. Automatic Topic Term Detection and Sentiment Classification for Opinion Mining [D]. Master Thesis. Saarbrücken, Germany: The University of Saarland, 2007. [6] S. Bethard, H. Yu, A. Thornton, V. Hatzivassiloglou, and D. Jurafsky. Automatic Extraction of Opinion Propositions and their Holders [A]. In: J. G. Shanahan et al. (eds). Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications [C]. Stanford, USA: 2004. [7] S.-M. Kim and E. Hovy. Extracting Opinions, Opinion Holders, and Topics Expressed in Online News Media Text [A]. In: Proceedings of the Workshop on Sentiment and Subjectivity in Text (COLING-ACL 2006 Workshop) [C]. Sydney, Australia: 2006, 1-8. [8] Y Choi, C. Cardie; E. Riloff and S. Patwardhan. Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns [A]. In: Proceedings of Human Language Technologies Conference / Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP 2005) [C]. Vancouver, Canada: 2005, 355-362. [9] V. Stoyanov and C. Cardie. Toward Opinion Summarization: Linking the Sources [A]. In: Proceddings of the Workshop on Sentiment and Subjectivity in Text (COLING-ACL 2006 Workshop) [C]. SydneyAustralia: 2006, 9-14. [10] B. Pang and L. Lee. A Sentimental Education: Sentiment Analysis using Subjectivity Summarization based on Minimun Cuts [A]. In: Proceedings of the ACL-2004 [C]. Barcelona, Spain: 2004, 271-278. [11] J. Wiebe, T. Wilson, R. Bruce, M. Bell, and M. Martin. Learning Subjective Language [A]. Technical Report TR-02-100 [C]. Department of Computer Science, University of Pittsburgh, Pittsburgh, Pennsylvania, USA: 2002. [12] T. Nasukawa and J. Yi. Sentiment Analysis: Capturing Favorability using Natural Language Processing [A]. In: Proceedings of the 2nd International Conference on Knowledge Capture (K-CAP 2003) [C]. Sanibel lsland, Florida, USA: 2003, 70-77. [13] V. Hatzivassiloglou and J. Wiebe. Effects of Adjective Orientation and Gradability on Sentence Subjectivity [A]. In: Proceedings of 18th International Conference on Computational Linguistics (COLING-2000) [C]. New Brunswick, NJ, USA: 2000. [14] K. Nigam and M. Hurst. Towards a Robust Metric of Opinion [A]. In: Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications [C]. Standford, USA: 2004. [15] H. Yu and V. Hatzivassiloglou. Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences [A]. In: M. Collins and M. Steedman (eds): Proceedings of EMNLP-03, 8th Conference on Empirical Methods in Natural Language Processing [C]. Sapporo, Japan: 2003, 129-136. [16] P. D. Turney. Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews [A]. In: Proceedings of ACL-02, 40th Annual Meeting of the Association for Computational Linguistics [C]. Philadelphia, USA: 2002, 417-424. [17] T. Wilson, J. Wiebe, and P. Hoffmann. Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis [A]. In: Proceedings of Human Language Technologies Conference/Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP 2005) [C]. Vancouver, Canada: 2005, 347-354. [18] M.M. Bradley, and P.J. Lang. Affective Norms for English Words (ANEW): Stimuli, Instruction Manual and Affective Ratings [A]. Technical report C-1 [C]. Gainesville, FL. The Center for Research in Psychophysiology, University of Florida, Florida, USA: 1999. [19] P. D. Turney and M. L. Littman. Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus [A]. Technical Report ERC-1094 (NRC 44929) [C]. National Research Council of Canada: 2002.

[20] M. Gamon and A. Aue. Automatic Identification of Sentiment Vocabulary: Exploiting Low Association with Known Sentiment Terms [A]. In: Proceedings of the ACL- 2005 Workshop on Feature Engineering for Machine Learning in NLP [C]. Michigan, USA: 2005, 57-64. [21] J. Kamps, M. Marx, R. J. Mokken, and M. de Rijke. Words with Attitude [A]. In: Proceddings of the 1st International Conference on Global WordNet [C]. Mysore, India: 2002. [22] B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up? Sentiment Classification using Machine Learning Techniques [A]. In: Proceedings of EMNLP-02, the Conference on Empirical Methods in Natural Language Processing [C]. Philadelphia, USA: 2002, 79-86. [23] K Dave, S. Lawrence, D. M. Pennock. Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews [A]. In: Proceedings of the 12th International World Wide Web Conference (WWW2003) [C]. Budapest, Hungary: 2003. [24] T. Kudo and Y Matsumoto. A Boosting Algorithm for Classification of Semi-structured Text [A]. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-2004) [C]. Barcelona, Spain: 2004, 301-308. [25] SMatsumoto, H. Takamura, and M. Okumura. Sentiment Classification using Word Sub-sequences and Dependency Sub-trees [A]. In: Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-05) [C]. Hanoi, Vietnam: 2005, 301-310. [26] T. Mullen, and N. Collier. Sentiment Analysis using Support Vector Machines with Diverse Information Sources [A]. In: Dekang Lin and Dekai Wu (eds.): Proceedings of EMNLP-04, 9th Conference on Empirical Methods in Natural Language Processing [C]. Barcelon, Spain: 2004, 412-418. [27] M. Gamon, A. Aue, S. Corston-Oliver, and E. Ringger. Pulse: Mining Customer Opinions from Free Text [A]. In: Proceedings of IDA-05, the 6th International Symposium on Intelligent Data Analysis [C]. Lecture Notes in Computer Science, Springer-Verlag. Madrid, Spain: 2005, 121-132. [28] B. Liu, M. Hu, and J. Cheng. Opinion Observer: Analyzing and Comparing Opinions on the Web [A]. In: Proceedings of WWW '05, the 14th international conference on World Wide Web [C]. Chiba, Japan: 2005, 342-351. [29] J. Yi and W. Niblack. Sentiment Mining in WebFountain [A]. In: Proceedings ICDE-05, the 21st International Conference on Data Engineering [C]. IEEE Computer Society. Tokyo, Japan: 2005, 1073-1083. [30] Theresa Wilson, Paul Hoffmann, Swapna Somasundaran, Jason Kessler, JanyceWiebe, Yejin Choi, Claire Cardie, Ellen Riloff, Siddharth Patwardhan. OpinionFinder: A System for Subjectivity Analysis [A]. In: Proceedings of HLT/EMNLP 2005 Demonstration Abstracts [C]. Vancouver, Canada: 2005, 34-35. [31] P. D. Turney and M. L. Littman. Measuring Praise and Criticism: Inference of Semantic Orientation from Association [J]. ACM Transactions on Information Systems (TOIS), 2003, 21(4): 315-346. [32] R. Yuan et al. Morpheme-based Derivation of Bipolar Semantic Orientation of Chinese Words [A]. In: Proceedings of the 20th International Conference on Computational Linguistics (COLING-2004) [C]. Geneva, Switzerland: 2004, 1008-1014. [33] B. Tsou, R. Yuen, O. Kwong, T. Lai, and W. Wong. Polarity Classification of Celebrity Coverage in the Chinese Press [A]. In: Proceedings of the International Conference on Intelligence Analysis [C]. McLean, USA: 2005. [34] 䚅ゟസˈ⿟㭛ˈ啭ᖫ⼢ˈᄭ࿛ढ. 䴶৥ BBS ⱘ䆱乬ᣪᥬ߱᥶ [A]. 㾕˖ᄭ㣖ᵒ䰜㕸⾔Џ㓪㞾✊䇁㿔⧚㾷Ϣ໻㾘῵‫ݙ‬ᆍ䅵ㅫ[C]࣫Ҁ⏙ढ໻ᄺߎ⠜⼒2005, 401-407. [35] Y. Xia, K.-F. Wong, and W. Li. A Phonetic-Based Approach to Chinese Chat Text Normalization [A]. In: Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (COLING-ACL 2006) [C]. Sydney, Australia: 2006, 993-1000.

[36] ᴅႷቮˈ䯉䫺ˈ਼䲙‫ˈ׽‬咘㨅㦕ˈਈゟᖋ෎Ѣ +RZ1HW ⱘ䆡∛䇁Нؒ৥䅵ㅫ[J]Ё᭛ֵᙃᄺ᡹2006, 20(1): 14-20. [37] ࿘ᖋ៤, ྮ໽ᯝ. ∝䇁হᄤ䇁Нᵕᗻߚᵤ੠㾖⚍ᢑপᮍ⊩ⱘⷨお [J]. 䅵ㅫᴎᑨ⫼, 26(11), 2006ˈ 2622-2625. [38] ᕤ⨇ᅣˈᵫ吓亲ˈᴼᖫ䈾. ෎Ѣ䇁Н⧚㾷ⱘ᭛ᴀؒ৥ᗻ䆚߿ᴎࠊ [J]. Ё᭛ֵᙃᄺ᡹2007, 21(1): 96-100. [39] ྮ໽ᯝˈ㘖䴦䰇ˈᴢᓎ䍙ˈᴢᵫ⨇ˈ࿘ᖋ៤ˈ䰜⦖ˈҬᅛ. ϔϾ⫼Ѣ∝䇁≑䔺䆘䆎ⱘᛣ㾕ᣪᥬ㋏㒳 [A]. 㾕˖᳍ে⧺ˈᄭ㣖ᵒЏ㓪ˈЁ᭛ֵᙃ໘⧚ࠡ⊓䖯ሩ-Ё೑Ё᭛ֵᙃᄺӮѠकѨ਼ᑈᄺᴃӮ䆂䆎᭛䲚 [C]. ࣫Ҁ: ⏙ढ໻ᄺߎ⠜⼒, 2006, 260-281.