A sentiment analysis model for small-scale unstructured policy data using transfer learning <sup>†</sup>
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전체 글
(2) 406. Soonjae Ahn · Seungeui Ryu · Soongoo Hong. 누 ᆫ ᅳ ᆫ 메 ᄌᄀ ᅡᄇ ᆯᄉ ᅡ ᆼᄃ ᅢ ᆫᄃ ᅬ ᅡ. ᄉ ᆯᄌ ᅵ ᅦᄅ ᅩᄌ ᆼᄎ ᅥ ᆨᄇ ᅢ ᆫᄋ ᅮ ᅣᄋ ᅦᄉ ᅥᄂ ᆫᄇ ᅳ ᅵᄌ ᆼᅧ ᅥ ᆼ ᄒᄃ ᅦᄋ ᅵᄐ ᅥᄋ ᅴᄀ ᅨᄅ ᆼᄒ ᅣ ᅪᄅ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄉ ᅵᄃ ᅩᄒ ᅡᄂ ᆫᄃ ᅳ ᆸᄅ ᅵ ᅥᄂ ᆼᄋ ᅵ ᅴᄀ ᆼᄋ ᅧ ᅮ ᅦᄋ ᄃ ᅵᅥ ᄐᄉ ᅮᄌ ᆸᄋ ᅵ ᅵᄆ ᅢᄋ ᅮᄌ ᅦᄒ ᆫᄌ ᅡ ᆨᄋ ᅥ ᅵᄀ ᅩᄃ ᅦᄋ ᅵᄐ ᅥᄃ ᅩᄒ ᆫᄇ ᅡ ᆫᄋ ᅮ ᅣᄋ ᅦᄎ ᅵᄌ ᆼᄃ ᅮ ᅬᄋ ᅥᄋ ᆻᄋ ᅵ ᅥᄃ ᅦᄋ ᅵᄐ ᅥᄇ ᅮᄌ ᆨᅮ ᅩ ᆫ ᄆᄌ ᅦᄀ ᅡᄌ ᅡᄌ ᅮᄇ ᆯᄉ ᅡ ᆼᄒ ᅢ ᆫᄃ ᅡ ᅡ (Bughin ᄃ ᆼ, 2018). ᅳ ᅵᅥ ᄋ ᄅᄒ ᆫᄃ ᅡ ᅦᄋ ᅵᄐ ᅥᄇ ᅮᄌ ᆨᄋ ᅩ ᅴᄒ ᆫᄀ ᅡ ᅨᄅ ᆯᄀ ᅳ ᆨᄇ ᅳ ᆨᄒ ᅩ ᅡᄀ ᅵᄋ ᅱᅡ ᆫ ᄒᄇ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅳᄅ ᅩᄌ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸ (Transfer learning)ᄋ ᅳ ᅵᄋ ᆻᄃ ᅵ ᅡ. ᄃ ᆸᄅ ᅵ ᅥᄂ ᆼ ᅵ ᅵᄇ ᄀ ᆫᄋ ᅡ ᅴᄀ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄆ ᅥ ᅩᄃ ᆯᄋ ᅦ ᅦᄉ ᅥᄀ ᅡᄌ ᆼᄌ ᅡ ᆼᄋ ᅮ ᅭᄒ ᆫᄋ ᅡ ᅭᄉ ᅩᄂ ᆫᄃ ᅳ ᅦᄋ ᅵᄐ ᅥᄋ ᅴᄋ ᆼᄋ ᅣ ᅵᄃ ᅡ. ᄀ ᅵᄌ ᆫᄋ ᅩ ᅴᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄌ ᅳ ᆨᄋ ᅥ ᆫᄃ ᅳ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄋ ᅳ ᅵ ᆼᄒ ᅭ ᄋ ᅡᄋ ᅧᄃ ᆸᄅ ᅵ ᅥᄂ ᆼᄀ ᅵ ᅵᄇ ᆫᄋ ᅡ ᅴᄀ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄆ ᅥ ᅩᄃ ᆯᄋ ᅦ ᆯᄆ ᅳ ᆫᄃ ᅡ ᆯᄀ ᅳ ᅵᄋ ᅱᄒ ᅡᄋ ᅧᄆ ᅩᄃ ᆯᄋ ᅦ ᅴᄑ ᅡᄅ ᅡᄆ ᅵᄐ ᅥ, ᄋ ᆸᄅ ᅵ ᆨᄎ ᅧ ᅢᄂ ᆯᄉ ᅥ ᅮᄃ ᆼᅳ ᅳ ᆯ ᄋᄉ ᅮᄌ ᆼᄒ ᅥ ᅢᄀ ᅡᄆ ᅧ ᆼᄂ ᅥ ᄉ ᆼᅳ ᅳ ᆯ ᄋᄒ ᆼᅡ ᅣ ᆼ ᄉᄉ ᅵᄏ ᅵᄀ ᅩᄌ ᅡᄒ ᅡᄋ ᆻᄌ ᅧ ᅵᄆ ᆫᄋ ᅡ ᅡᄌ ᆨᄁ ᅵ ᅡᄌ ᅵᄏ ᆫᄉ ᅳ ᆼᄀ ᅥ ᅪᄅ ᆯᄇ ᅳ ᅩᄋ ᅵᄀ ᅩᄋ ᆻᄌ ᅵ ᅵᄋ ᆭᄃ ᅡ ᅡ (Kim ᄃ ᆼ, 2016). ᄌ ᅳ ᆫᄐ ᅥ ᆼᄌ ᅩ ᆨᄋ ᅥ ᆫᄃ ᅵ ᆸᄅ ᅵ ᅥᄂ ᆼ ᅵ ᆯᄀ ᅡ ᄋ ᅩᄅ ᅵᄌ ᆷᄋ ᅳ ᆫᄐ ᅳ ᆨᄌ ᅳ ᆼᄀ ᅥ ᅪᄌ ᅦᄂ ᅡᄆ ᆫᄌ ᅮ ᅦᄅ ᆯᄒ ᅳ ᅢᄀ ᆯᄒ ᅧ ᅡᄀ ᅵᄋ ᅱᄒ ᅢᄒ ᆨᄉ ᅡ ᆸᄒ ᅳ ᅡᄃ ᅩᄅ ᆨᄉ ᅩ ᆯᄀ ᅥ ᅨᄃ ᅬᄋ ᅥᄋ ᆻᄃ ᅵ ᅡ. ᄌ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᆫᄉ ᅳ ᅡᄅ ᆷᄋ ᅡ ᅵᄋ ᅧᄅ ᅥᄀ ᅪᄌ ᅦ ᆯᄂ ᅳ ᄅ ᆷᄂ ᅥ ᅡᄃ ᆯᄆ ᅳ ᆫᄉ ᅧ ᅥᄌ ᅵᄉ ᆨᄋ ᅵ ᆯᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅡᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅵᄋ ᅡᄂ ᅵᄅ ᅡᄌ ᆨᅥ ᅵ ᆸ ᄌᄌ ᆨᄋ ᅥ ᅵᄀ ᅩᄒ ᆫᄃ ᅡ ᆫᄀ ᅡ ᅨᄃ ᅥᄇ ᆯᄌ ᅡ ᆫᄃ ᅥ ᆫᄇ ᅬ ᆼᄉ ᅡ ᆨᄋ ᅵ ᅳᄅ ᅩᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄇ ᅳ ᅢᄋ ᅯᄂ ᅡ ᆫᄃ ᅡ ᄀ ᅡ (Caruana ᄃ ᆼ, 1995). ᄌ ᅳ ᆨ, ᄋ ᅳ ᅵᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᆫᄃ ᅳ ᅡᄅ ᆫᄀ ᅳ ᆫᄅ ᅪ ᆫᄃ ᅧ ᆫᄀ ᅬ ᅪᄌ ᅦᄋ ᅦᄃ ᅢᄒ ᆫᄌ ᅡ ᅵᄉ ᆨᄋ ᅵ ᅵᄂ ᅡᄆ ᅩᄃ ᆯᄋ ᅦ ᆯᄌ ᅳ ᅢᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄂ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᆯ ᅳ ᅡᄋ ᄉ ᆼᄒ ᅭ ᆫᄃ ᅡ ᅡ. ᄋ ᆼᄉ ᅧ ᆼᄎ ᅡ ᅥᄅ ᅵᄇ ᆫᄋ ᅮ ᅣᄋ ᅦᄉ ᅥᄂ ᆫᄌ ᅳ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅵᄆ ᆭᄋ ᅡ ᅵᄉ ᅡᄋ ᆼᄃ ᅭ ᅬᄀ ᅩᄋ ᆻᄋ ᅵ ᆻᄌ ᅥ ᅵᄆ ᆫ (Heoᄋ ᅡ ᅪ Lim, 2019) ᄒ ᆫᄀ ᅡ ᆯᄐ ᅳ ᆨᄉ ᅦ ᅳᄐ ᅳᄋ ᅦ ᅥᄂ ᄉ ᆫᅥ ᅳ ᄀᄋ ᅴᄉ ᅡᄋ ᆼᄃ ᅭ ᅬᄀ ᅩᄋ ᆻᄌ ᅵ ᅵᄋ ᆭᄃ ᅡ ᅡ. ᆫᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄋ ᅴᄆ ᆨᄌ ᅩ ᆨᄋ ᅥ ᆫᄉ ᅳ ᅩᄀ ᅲᄆ ᅩᄌ ᆼᅢ ᅥ ᆨ ᄎᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄃ ᅳ ᅢᄉ ᆼᄋ ᅡ ᅳᄅ ᅩᄌ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋᄒ ᆯᄋ ᅪ ᆼᄒ ᅭ ᆫᄀ ᅡ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄆ ᅥ ᅩᄃ ᆯᄋ ᅦ ᆯᄌ ᅳ ᅦᄋ ᆫᄒ ᅡ ᅡᄀ ᅩᄆ ᅩᄃ ᆯ ᅦ ᅴᄐ ᄋ ᅡᄃ ᆼᄉ ᅡ ᆼᄋ ᅥ ᆯᄀ ᅳ ᆷᄌ ᅥ ᆼᄒ ᅳ ᅡᄂ ᆫᄃ ᅳ ᅦᄋ ᆻᄃ ᅵ ᅡ. ᄉ ᅦᄇ ᅮᄌ ᆨᆫ ᅥ ᄋ ᅵᅧ ᆫ ᄋᄀ ᅮᄅ ᅩᄎ ᆺᄍ ᅥ ᅢ, ᄃ ᅢᄅ ᆼᄋ ᅣ ᅴᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄀ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄆ ᅥ ᅩᄃ ᆯᄋ ᅦ ᆯᄉ ᅳ ᆼᅥ ᅢ ᆼ ᄉ ᅡᄀ ᄒ ᅩᅮ ᆯ ᄃᄍ ᅢ, ᄉ ᅩᄀ ᅲᄆ ᅩᄆ ᆨᄑ ᅩ ᅭᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄃ ᅳ ᅢᄉ ᆼᄋ ᅡ ᅳᄅ ᅩᄌ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᄒ ᅳ ᅡᄋ ᅧᄀ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄆ ᅥ ᅩᄃ ᆯᄋ ᅦ ᅴᄀ ᅡᄌ ᆼᄎ ᅮ ᅵᄅ ᆯᄌ ᅳ ᅩᄌ ᆯᄒ ᅥ ᆫᄃ ᅡ ᅡ. ᄉ ᆺᄍ ᅦ ᅢ, ᄌ ᅦ ᆫᄃ ᅡ ᄋ ᆫᅩ ᅬ ᄆᄃ ᆯᄋ ᅦ ᅴᄀ ᆷᄌ ᅥ ᆼᄋ ᅳ ᆯᄋ ᅳ ᅱᄒ ᅡᄋ ᅧᄉ ᅩᄅ ᆼᄋ ᅣ ᅴᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄆ ᆫᄃ ᅡ ᆫᄀ ᅳ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄆ ᅥ ᅩᄃ ᆯᄀ ᅦ ᅪᄌ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋᄉ ᅡᄋ ᆼᄒ ᅭ ᆫᄀ ᅡ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫ ᅮ ᆨᄆ ᅥ ᄉ ᅩᅦ ᄃᄋ ᆯ ᅴᄌ ᆼᄒ ᅥ ᆨᄃ ᅪ ᅩᅪ 아 ᆨ ᄒᄉ ᆸᄉ ᅳ ᅵᄀ ᆫᄋ ᅡ ᆯᄇ ᅳ ᅵᄀ ᅭᄒ ᆫᄃ ᅡ ᅡ.. 2. 선행연구 ᆷᄉ ᅡ ᄀ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄋ ᅥ ᆫᄐ ᅳ ᆨᄉ ᅦ ᅳᄐ ᅳᄆ ᅡᄋ ᅵᄂ ᆼᄋ ᅵ ᅴᅡ ᆫ ᄒᄇ ᆫᄋ ᅮ ᅣᄅ ᅩᄌ ᅡᄋ ᆫᄋ ᅧ ᅥᄎ ᅥᄅ ᅵᄆ ᆾᄀ ᅵ ᆫᄅ ᅪ ᆫᅥ ᅧ ᆷ ᄏᄑ ᅲᄐ ᅥᄀ ᅵᄉ ᆯᄋ ᅮ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄐ ᆨᄉ ᅦ ᅳᄐ ᅳᄋ ᅴᄀ ᆷᄉ ᅡ ᆼ ᅥ ᄋᄌ ᆯ ᅳ ᅡᅩ ᆼ ᄃᄋ ᅳᄅ ᅩᄎ ᅮᄎ ᆯᄒ ᅮ ᅡᄀ ᅥᄂ ᅡᄇ ᆫᄅ ᅮ ᅲᄒ ᅡᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᆯᄆ ᅳ ᆯᅡ ᅡ ᆫ ᄒᄃ ᅡ (Zhang ᄃ ᆼ, 2018). ᄀ ᅳ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄋ ᅥ ᅦᄂ ᆫᄆ ᅳ ᅢᄋ ᅮᄃ ᅡᄋ ᆼᅡ ᅣ ᆫ ᄒᄋ ᆼᄋ ᅳ ᆼᅮ ᅭ ᆫ ᄇᄋ ᅣ ᅡᄋ ᄀ ᆻᅡ ᅵ ᄃ. ᄋ ᅨᄅ ᆯᄃ ᅳ ᆯᄋ ᅳ ᅥᄉ ᅩᄇ ᅵᄌ ᅡᄉ ᆫᄋ ᅡ ᆸᄋ ᅥ ᅦᄉ ᅥᄌ ᅦᄑ ᆷᄅ ᅮ ᅵᄇ ᅲᄀ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄋ ᅥ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄃ ᅡᄋ ᆼᅡ ᅣ ᆫ ᄒᄉ ᆼᄑ ᅡ ᆷᄋ ᅮ ᅦᄃ ᅢᄒ ᆫᄉ ᅡ ᅡᄋ ᆼᄌ ᅭ ᅡᄉ ᆫᄒ ᅥ ᅩᄃ ᅩᄅ ᆯᄒ ᅳ ᆨ ᅪ ᅩᄒ ᄇ ᅡᅧ ᄋᄒ ᅬᄉ ᅡᄀ ᅡᄑ ᆫᄆ ᅡ ᅢᄌ ᆫᄅ ᅥ ᆨᄋ ᅣ ᆯᄌ ᅳ ᅩᄌ ᆼᄒ ᅥ ᅡᄀ ᅩᄋ ᅴᄉ ᅡᄀ ᆯᅥ ᅧ ᆼ ᄌᄋ ᆯᄂ ᅳ ᅢᄅ ᆯᄉ ᅵ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᄉ ᅩᄉ ᆯᄆ ᅧ ᅵᄃ ᅵᄋ ᅥᄋ ᅦᄉ ᅥᄋ ᅵᄇ ᆫᄐ ᅦ ᅳᄃ ᆺᄀ ᅢ ᆯᄋ ᅳ ᅦᄃ ᅢᄒ ᆫᄀ ᅡ ᆷ ᅡ ᆼᄇ ᅥ ᄉ ᆫᅥ ᅮ ᆨ ᄉᄋ ᆫᄉ ᅳ ᅡᄋ ᆼᄌ ᅭ ᅡᄀ ᅡᄑ ᆯᄋ ᅵ ᅭᄅ ᅩᄒ ᅡᄂ ᆫᄌ ᅳ ᆼᄇ ᅥ ᅩᄅ ᆯᄈ ᅳ ᅡᄅ ᅳᄀ ᅦᄎ ᆽᄋ ᅡ ᅡᄂ ᅢᄀ ᅩ, ᄋ ᅴᄆ ᅵᄋ ᆻᄂ ᅵ ᆫᄌ ᅳ ᆼᄇ ᅥ ᅩᄅ ᆯᄋ ᅳ ᅲᄎ ᅮᄒ ᅢᄂ ᅢᄂ ᆫᄃ ᅳ ᅦᄌ ᆼᄋ ᅮ ᅭᄒ ᆫᄋ ᅡ ᆨᄒ ᅧ ᆯᄋ ᅡ ᆯ ᅳ ᆫᄃ ᅡ ᄒ ᅡ (Salas-Zarate ᄃ ᆼ, 2016; Alashri ᄃ ᅳ ᆼ, 2016; Mertiyaᄋ ᅳ ᅪ Singh, 2016). 2000ᄂ ᆫᄎ ᅧ ᅩᄋ ᅵᄅ ᅢᄅ ᅩᄀ ᆷᄌ ᅡ ᆼᄇ ᅥ ᆫ ᅮ ᆨᄋ ᅥ ᄉ ᆫᄌ ᅳ ᅡᄋ ᆫᄋ ᅧ ᅥᄎ ᅥᄅ ᅵ (National Language Processing; NLP)ᄋ ᅦᄉ ᅥᄀ ᅡᄌ ᆼᄒ ᅡ ᆯᄇ ᅪ ᆯᅡ ᅡ ᆫ 혀 ᆫ ᄋᄀ ᅮᄇ ᆫᄋ ᅮ ᅣᄌ ᆼᄒ ᅮ ᅡᄂ ᅡᄀ ᅡᅬ ᄃᄋ ᆻᄃ ᅥ ᅡ (Hussein, 2018). ᅵᄌ ᄀ ᆫᄋ ᅩ ᅴᄀ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄋ ᅥ ᆯᄋ ᅳ ᅱᄒ ᆫᄌ ᅡ ᆸᄀ ᅥ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅳᄅ ᅩᄂ ᆫᄋ ᅳ ᅥᄒ ᅱᄀ ᅵᄇ ᆫ (Lexicon-based approach)ᄀ ᅡ ᅪᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄀ ᅳ ᅵᄇ ᆫ (Maᅡ chine learning approach)ᄋ ᅴᄇ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅳᄅ ᅩᄂ ᅡᄂ ᆯᄉ ᅮ ᅮᄋ ᆻᄃ ᅵ ᅡ (Chungᄀ ᅪ Ahn, 2019). ᄋ ᅥᄒ ᅱᄀ ᅵᄇ ᆫᄋ ᅡ ᅴᄀ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄋ ᅥ ᆫᄌ ᅳ ᆫ ᅥ ᆼᄌ ᅩ ᄐ ᆨᅵ ᅥ ᄋᄀ ᆫ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄇ ᅥ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅳᄅ ᅩᄀ ᆷᄉ ᅡ ᆼᄉ ᅥ ᅡᄌ ᆫᄋ ᅥ ᆯᄀ ᅳ ᅮᄎ ᆨᄒ ᅮ ᅡᄀ ᅩᄉ ᅡᄌ ᆫᄋ ᅥ ᅦᄀ ᅵᄅ ᆨᄃ ᅩ ᆫᄀ ᅬ ᆷᄉ ᅡ ᆼᄃ ᅥ ᆫᄋ ᅡ ᅥᄋ ᅴᄌ ᆷᄉ ᅥ ᅮᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄀ ᆷᄉ ᅡ ᆼᄋ ᅥ ᅴᄀ ᆨ ᅳ ᆼᄋ ᅥ ᄉ ᆯᄑ ᅳ ᆫᅡ ᅡ ᆫ 다 ᆫ ᄒᄃ ᅡ (Turneyᄋ ᅪ Littman, 2003; Huᄋ ᅪ Liu, 2004; Bravo-Marquez ᄃ ᆼ, 2016; Yang ᄃ ᅳ ᆼ, 2013). ᅳ ᅥᄒ ᄋ ᅱᅵ ᄀᄇ ᆫᄌ ᅡ ᆸᄀ ᅥ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᆫᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᅡᄀ ᅵᄉ ᆸᄌ ᅱ ᅵᄆ ᆫᄀ ᅡ ᆷᄉ ᅡ ᆼᄉ ᅥ ᅡᄌ ᆫᄋ ᅥ ᅦᄏ ᅳᄀ ᅦᄋ ᅴᄌ ᆫᄒ ᅩ ᅡᄀ ᅩᄃ ᆫᄋ ᅡ ᅥᄉ ᅡᄋ ᅵᄋ ᅴᄋ ᅱᄎ ᅵᄋ ᆫᄀ ᅧ ᆯᄋ ᅧ ᆯᄆ ᅳ ᅮᄉ ᅵᄒ ᅡᄋ ᅧᄆ ᅢ ᅮᄃ ᄋ ᆫᄉ ᅡ ᆫᄒ ᅮ ᅡᄃ ᅡᄂ ᆫᄃ ᅳ ᆫᄌ ᅡ ᆷᄋ ᅥ ᅵᄋ ᆻᄃ ᅵ ᅡ. ᄎ ᅬᄀ ᆫᄋ ᅳ ᅦᄂ ᆫᄃ ᅳ ᆸᄅ ᅵ ᅥᄂ ᆼᄋ ᅵ ᅵᄏ ᅳᄀ ᅦᄇ ᆯᄌ ᅡ ᆫᄒ ᅥ ᆷᄋ ᅡ ᅦᄄ ᅡᄅ ᅡᄃ ᆸᄅ ᅵ ᅥᄂ ᆼᄋ ᅵ ᆯᄀ ᅳ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄋ ᅥ ᅦᄌ ᆨᄋ ᅥ ᆼᄒ ᅭ ᅡᄂ ᆫᄀ ᅳ ᆺ ᅥ ᅵᄃ ᄋ ᅥᅮ ᆨ ᄋᄃ ᅢᄌ ᆼᄒ ᅮ ᅪᄃ ᅬᄋ ᆻᄃ ᅥ ᅡ. Bag of Words (BoW) ᄀ ᅪᅡ ᆫ ᄃᄋ ᅥᄋ ᆷᄇ ᅵ ᅦᄃ ᆼᄋ ᅵ ᆫᄋ ᅳ ᆯᄇ ᅵ ᆫᄌ ᅡ ᆨᄋ ᅥ ᅳᄅ ᅩᄉ ᅡᄋ ᆼᄃ ᅭ ᅬᄂ ᆫᄃ ᅳ ᆸᄅ ᅵ ᅥᄂ ᆼᄆ ᅵ ᅩᄃ ᆯᄋ ᅦ ᅦᄉ ᅡ ᆼᄃ ᅭ ᄋ ᆫᅡ ᅬ ᄃ. ᄃ ᆫᄋ ᅡ ᅥᄋ ᆷᄇ ᅵ ᅦᄃ ᆼᄆ ᅵ ᅩᄃ ᆯᄋ ᅦ ᅴᄃ ᅢᄑ ᅭᄌ ᆨᄋ ᅥ ᆫᄋ ᅵ ᅨᄂ ᆫ Word2Vecᄋ ᅳ ᅵᄃ ᅡ (Mikolov ᄃ ᆼ, 2013a; Mikolov ᄃ ᅳ ᆼ, 2013b). ᅳ ᆷᄉ ᅡ ᄀ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄋ ᅥ ᅦᄉ ᅡᄋ ᆼᄃ ᅭ ᅬᄂ ᆫᄃ ᅳ ᆸᄅ ᅵ ᅥᄂ ᆼᄇ ᅵ ᆼᄇ ᅡ ᆸᄌ ᅥ ᆼᄎ ᅮ ᅬᄀ ᆫᄀ ᅳ ᅡᄌ ᆼᄆ ᅡ ᆭᄋ ᅡ ᅵᄉ ᅡᄋ ᆼᄃ ᅭ ᅬᄂ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᆫ CNN (Convolutional Neural ᅳ Network)ᄋ ᅵᄃ ᅡ. CNNᄋ ᆫᄎ ᅳ ᅥᄋ ᆷᄋ ᅳ ᅦᄂ ᆫᄋ ᅳ ᅵᄆ ᅵᄌ ᅵᄅ ᆯᄎ ᅳ ᅥᄅ ᅵᄒ ᅡᄀ ᅩᄏ ᆷᄑ ᅥ ᅲᄐ ᅥᄇ ᅵᄌ ᆫᄋ ᅥ ᅦᄉ ᅥᄋ ᅮᄉ ᅮᄒ ᆫᅧ ᅡ ᆯ ᄀᄀ ᅪᄅ ᆯᄃ ᅳ ᆯᄉ ᅡ ᆼᄒ ᅥ ᅡᄂ ᆫᄃ ᅳ ᅦᄉ ᅡᄋ ᆼ ᅭ ᅬᄋ ᄃ ᆻᅡ ᅥ ᄃ (Lu ᄃ ᆼ, 2018). ᄎ ᅳ ᅬᄀ ᆫᄆ ᅳ ᆾᅧ ᅧ ᆫ ᄂᄃ ᆼᄋ ᅩ ᆫ CNNᄋ ᅡ ᆫ NLPᄋ ᅳ ᅦᄃ ᅩᄄ ᆨᄀ ᅩ ᇀᄋ ᅡ ᅵᄒ ᅭᅪ ᄀᄌ ᆨᄋ ᅥ ᅵᄆ ᅧᄐ ᆨᄉ ᅦ ᅳᄐ ᅳᄇ ᆫᄉ ᅮ ᆨᄋ ᅥ ᅦᄉ ᅥᄒ ᆯᅲ ᅮ ᆼ ᄅᄒ ᆫ ᅡ ᆯᄀ ᅧ ᄀ ᅪᄅ ᆯᄋ ᅳ ᆮᄂ ᅥ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅳᄅ ᅩᄋ ᆸᄌ ᅵ ᆼᄃ ᅳ ᅬᄋ ᆻᄃ ᅥ ᅡ (Kim, 2014, Hong ᄃ ᆼ, 2019). Kim (2014)ᄋ ᅳ ᆫᄆ ᅳ ᆫᄌ ᅮ ᆼᄉ ᅡ ᅮᄌ ᆫᄋ ᅮ ᅦᄉ ᅥᄐ ᆨᄉ ᅦ ᅳᄐ ᅳᄅ ᆯ ᅳ ᆫᄅ ᅮ ᄇ ᅲᄒ ᅡᄀ ᅵᄋ ᅱᄒ ᅢᄉ ᅡᄌ ᆫᄒ ᅥ ᆫᄅ ᅮ ᆫᄃ ᅧ ᆫᄃ ᅬ ᆫᄋ ᅡ ᅥᄇ ᆨᄐ ᅦ ᅥᅪ ᄋᄀ ᆯᄒ ᅧ ᆸᄃ ᅡ ᆫ CNN ᄆ ᅬ ᅩᄒ ᆼᄋ ᅧ ᆯᄌ ᅳ ᅦᄋ ᆫᄒ ᅡ ᅡᄀ ᅩᄆ ᅩᄒ ᆼᄋ ᅧ ᅴᄋ ᅮᄉ ᅮᄉ ᆼᄋ ᅥ ᆯᄀ ᅳ ᆷᄌ ᅥ ᆼᄒ ᅳ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄒ ᅡ ᅵᄆ ᄌ ᆫᄒ ᅡ ᆫᄀ ᅡ ᆨᄋ ᅮ ᅥᄋ ᅴᄐ ᆨᄉ ᅦ ᅳᄐ ᅳᄇ ᆫᄅ ᅮ ᅲᄆ ᆾᄎ ᅵ ᅥᄅ ᅵᄅ ᆯᄒ ᅳ ᆯᄄ ᅡ ᅢᄋ ᅥᄌ ᆯᄀ ᅥ ᅵᄇ ᆫᄋ ᅡ ᅴ CNN ᄇ ᆫᄅ ᅮ ᅲᄀ ᅵᄇ ᆸᄋ ᅥ ᆯᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᅡᄆ ᆫᄇ ᅧ ᆫᄉ ᅮ ᆨᄒ ᅥ ᅡᄀ ᅵᄋ ᅥᄅ ᅧᄋ ᆫ ᅮ.
(3) A sentiment analysis model for small-scale unstructured policy data using transfer learning. 407. ᄃᄋ ᆫ ᅡ ᅥᄒ ᆨᄋ ᅩ ᆫᄆ ᅳ ᅵᄒ ᆨᄉ ᅡ ᆸᄃ ᅳ ᆫᄋ ᅡ ᅥ (Out of vocabulary)ᄀ ᅡᄃ ᆼᄌ ᅳ ᆼᄒ ᅡ ᅡᄂ ᆫᅮ ᅳ ᆫ ᄆᄌ ᅦᄀ ᅡᄇ ᆯᄉ ᅡ ᆼᄒ ᅢ ᆫᄃ ᅡ ᅡ. Hong ᄃ ᆼ (2019)ᄋ ᅳ ᆫᄋ ᅳ ᅵᄅ ᅥᄒ ᆫ ᅡ ᅥᄌ ᄋ ᆯᄀ ᅥ ᅵᄇ ᆫᄋ ᅡ ᅴ CNN ᄇ ᆫᄅ ᅮ ᅲᄀ ᅵᄇ ᆸᄋ ᅥ ᅴᄆ ᆫᄌ ᅮ ᅦᄅ ᆯᄒ ᅳ ᅢᄀ ᆯᄒ ᅧ ᅡᄀ ᅵᄋ ᅱᄒ ᅡᄋ ᅧᄒ ᆼᄐ ᅧ ᅢᄉ ᅩ, ᄋ ᆷᄌ ᅳ ᆯ, ᄌ ᅥ ᅡᄉ ᅩᄃ ᆫᄋ ᅡ ᅱᄅ ᅩᄇ ᆫᄉ ᅮ ᆨᄒ ᅥ ᅡᄂ ᆫᄆ ᅳ ᆯᄐ ᅥ ᅵᄎ ᅢᄂ ᆯ ᅥ CNN ᅩ ᄆᄃ ᆯᄋ ᅦ ᆯᄌ ᅳ ᅦᄋ ᆫᄒ ᅡ ᅡᄋ ᆻᄃ ᅧ ᅡ. CNN ᄆ ᅩᄃ ᆯᄋ ᅦ ᆫᄃ ᅳ ᆸᄅ ᅵ ᅥᄂ ᆼᄇ ᅵ ᆼᄇ ᅡ ᆸᄌ ᅥ ᆼᄇ ᅮ ᅵᄀ ᅭᄌ ᆨᄈ ᅥ ᅡᄅ ᆫᄒ ᅳ ᆨᄉ ᅡ ᆸᄉ ᅳ ᅵᄀ ᆫᄀ ᅡ ᅪᄂ ᇁᄋ ᅩ ᆫᄌ ᅳ ᆼᄒ ᅥ ᆨᄃ ᅪ ᅩᄅ ᆯᄇ ᅳ ᅩᄋ ᅵᄌ ᅵᄆ ᆫᄆ ᅡ ᅢᄋ ᅮᄆ ᆭᄋ ᅡ ᆫᄒ ᅳ ᆨᄉ ᅡ ᆸᄃ ᅳ ᅦᄋ ᅵᄐ ᅥ ᅡᄑ ᄀ ᆯᄋ ᅵ ᅭᄒ ᅡᄃ ᅡ. ᄆ ᅵᄃ ᅵᄋ ᅥ, SNS ᄃ ᅦᄋ ᅵᄐ ᅥᄂ ᆫᄆ ᅳ ᆭᄋ ᅡ ᅵᄌ ᆫᄌ ᅩ ᅢᄒ ᅡᄌ ᅵᄆ ᆫᄌ ᅡ ᆼᅢ ᅥ ᆨ ᄎᄇ ᆫᄋ ᅮ ᅣᄋ ᅴᄃ ᅦᄋ ᅵᄐ ᅥᄂ ᆫᄆ ᅳ ᅢᄋ ᅮᄇ ᅮᄌ ᆨᄒ ᅩ ᅡᄋ ᅧ CNN ᄇ ᆼ ᅡ ᆸᄋ ᅥ ᄇ ᆯᅩ ᅳ ᆼ ᄐᄒ ᅢᄒ ᆨᄉ ᅡ ᆸᄉ ᅳ ᅵᄏ ᅵᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅵᄆ ᅢᄋ ᅮᄋ ᅥᄅ ᆸᄃ ᅧ ᅡ (Bughin ᄃ ᆼ, 2018). ᄋ ᅳ ᅵᄅ ᅥᄒ ᆫᄒ ᅡ ᆫᄀ ᅡ ᅨᄅ ᆯᄀ ᅳ ᆨᄇ ᅳ ᆨᄒ ᅩ ᅡᄀ ᅵᄋ ᅱᄒ ᅡᄋ ᅧᅬ ᄎᄀ ᆫᄆ ᅳ ᆭ ᅡ ᆫᄋ ᅳ ᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄌ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᆯᄉ ᅳ ᅵᄃ ᅩᄒ ᅡᄀ ᅩᄋ ᆻᄃ ᅵ ᅡ. ᄌ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅵᄅ ᆫᄉ ᅡ ᆫᅧ ᅵ ᆼ ᄀᄆ ᆼᄋ ᅡ ᅴᄋ ᆯᄇ ᅵ ᅮᄄ ᅩᄂ ᆫᄌ ᅳ ᆫᄎ ᅥ ᅦᄉ ᆫᅧ ᅵ ᆼ ᄀᄆ ᆼᄀ ᅡ ᅡᄌ ᆼᄎ ᅮ ᅵᄑ ᅡᄅ ᅡᄆ ᅵ ᅥᄅ ᄐ ᆯᄎ ᅳ ᅬᄃ ᅢᄋ ᅮᄃ ᅩᄎ ᅮᄌ ᆼ (Maximum likelihood estimation)ᄋ ᅥ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄒ ᆨᄉ ᅡ ᆸᄃ ᅳ ᅦᄋ ᅵᄐ ᅥᄋ ᅦᄇ ᆫᄀ ᅩ ᆨᄌ ᅧ ᆨᄋ ᅥ ᅳᄅ ᅩᄒ ᆫᄅ ᅮ ᆫᄉ ᅧ ᅵᄏ ᅵᄀ ᅵᄋ ᅦ ᇁᄉ ᅡ ᄋ ᅥ, ᄃ ᅡᄅ ᆫᄃ ᅳ ᅦᄋ ᅵᄐ ᅥᄉ ᆺᄋ ᅦ ᅵᄂ ᅡᄆ ᆨᄌ ᅩ ᆨᄒ ᅥ ᆷᄉ ᅡ ᅮᄅ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᆫᄒ ᅡ ᅮ, ᄋ ᅵᄅ ᆯᄇ ᅳ ᅡᄐ ᆼᄋ ᅡ ᅳᄅ ᅩᄇ ᆫᄀ ᅩ ᆨᄌ ᅧ ᆨᄋ ᅥ ᆫᄒ ᅵ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅦᄉ ᅥᄉ ᆫᅧ ᅵ ᆼ ᄀᄆ ᆼᄀ ᅡ ᅡᄌ ᆼᄎ ᅮ ᅵᄑ ᅡᄅ ᅡ ᅵᄐ ᄆ ᅥᄅ ᆯᄃ ᅳ ᅥᄉ ᆸᄀ ᅱ ᅦᅬ ᄎᄌ ᆨᄒ ᅥ ᅪᄒ ᅡᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᆯᄆ ᅳ ᆯᅡ ᅡ ᆫ ᄒᄃ ᅡ. ᄌ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅴᄀ ᅡᄌ ᆼᄏ ᅡ ᆫᄌ ᅳ ᆼᄌ ᅡ ᆷᄋ ᅥ ᆫᄉ ᅳ ᅩᄉ ᅳᄇ ᆫᄋ ᅮ ᅣᄋ ᅴᄌ ᅵᄉ ᆨᄋ ᅵ ᆯᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅢᄉ ᅥᄐ ᅡ ᆺᄇ ᅵ ᄀ ᆫᄋ ᅮ ᅣᄋ ᅴᅡ ᆨ ᄒᄉ ᆸᅳ ᅳ ᆯ ᄋᄒ ᆼᅡ ᅣ ᆼ ᄉᄉ ᅵᄏ ᆯᄉ ᅵ ᅮᄋ ᆻᄋ ᅵ ᅥᄐ ᅡᄀ ᅵ ᆺᄇ ᆫᄋ ᅮ ᅣᄋ ᅴᄃ ᅦᄋ ᅵᄐ ᅥᄀ ᅡᄌ ᆨᄃ ᅥ ᅡᄆ ᆫᄃ ᅧ ᅢᄅ ᆼᄋ ᅣ ᅴᄉ ᅩᄉ ᅳᄇ ᆫᄋ ᅮ ᅣᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᅧ ᆼᄒ ᅥ ᄌ ᆨᅩ ᅪ ᄃᄅ ᆯᄒ ᅳ ᆼᅡ ᅣ ᆼ ᄉᄉ ᅵᄏ ᅵᄀ ᅩᄒ ᆨᄉ ᅡ ᆸᄉ ᅳ ᅵᄀ ᆫᄋ ᅡ ᆯᄃ ᅳ ᆫᄎ ᅡ ᆨᄉ ᅮ ᅵᄏ ᆯᄉ ᅵ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᄋ ᅵᄆ ᅵᄉ ᅮᄂ ᆫᄌ ᅧ ᆫᄇ ᅥ ᅮᄐ ᅥᄋ ᆼᄉ ᅧ ᆼᄎ ᅡ ᅥᄅ ᅵᄇ ᆫᄋ ᅮ ᅣᄋ ᅦᄉ ᅥᄂ ᆫᄌ ᅳ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸ ᅳ ᅵᄉ ᄋ ᅡᅭ ᆼ ᄋᄃ ᅬᄀ ᅩᄋ ᆻᄋ ᅵ ᆻᄌ ᅥ ᅵᄆ ᆫ (Heoᄋ ᅡ ᅪ Lim, 2019) ᄎ ᅬᄀ ᆫᄋ ᅳ ᅦᄂ ᆫᄋ ᅳ ᆼᄆ ᅧ ᆫᄐ ᅮ ᆨᄉ ᅦ ᅳᄐ ᅳᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄃ ᅳ ᅢᄉ ᆼᄋ ᅡ ᅳᄅ ᅩᄌ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅵᄌ ᆨᄋ ᅥ ᆼᄃ ᅭ ᅬ ᅩᄋ ᄀ ᆻᅳ ᅵ ᄋᄂ ᅡᄒ ᆫᄀ ᅡ ᆯᄐ ᅳ ᆨᄉ ᅦ ᅳᄐ ᅳᄇ ᆫᄉ ᅮ ᆨᄋ ᅥ ᅦᄉ ᅡᄋ ᆼᄃ ᅭ ᆫᄌ ᅬ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᆫᄋ ᅳ ᆹᄂ ᅥ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅳᄅ ᅩᄇ ᅩᄋ ᆫᄃ ᅵ ᅡ. Table 2.1 Transfer learning for sentiment analysis Research. Basic model / core idea. Sun et al. GRU (2014) McCann et al. SDA (2017) Peters et al. biLM (2018) Dai and Le. LSTM (2015) Howard et al. AWD-LSTM (2018) Dai et al. Boosting, SVM (2017) Xu et al. Transfer Self-training algorithm (2011) Gui et al. Rademacher distribution (2015) Wang et al. Adaptive source-domain training (2017) instance selection Blitzer et al. Identify correspondences among (2006) features from different domains Pan et al. construct a bipartite graph to model the (2010) co-occurrence relationship Xia et al. feature ensemble(FE), PCA-based (2013) sample selection(PCA-SS) Zhou G et al. a joint non-negative matrix (2015) factorization. Transfer learning methods Parameter -transfer. Datasets Amazon Product Review IMDB/SST-5. Instance -transfer. Feature -representation. Acc (%) 81.08. SST-5. 91.80/ 53.70 54.70. IMDB. 92.80. IMDB. 95.40. 20Newsgroups. 93.15. NTCIR-7 MOAT Copora. 73.04. NLP&CC 2013 cross-lingual opinion analysis dataset Blog, Diary, Exp, Fairy. 80.84 67.03. Source Domain: WSJ, Target 96.10 Domain: Biomedical Text Amazon Product Review 72.86/ 86.75 Amazon Product Review 72.50/ 84.87 Amazon Product Review 74.83/ 86.33.
(4) 408. Soonjae Ahn · Seungeui Ryu · Soongoo Hong. 3. 연구방법론 3.1. 학습데이터 셋 수집 ᆫᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄌ ᅳ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋᄋ ᅱᄒ ᅢᄃ ᅢᄅ ᆼᄋ ᅣ ᅴᄃ ᅦᄋ ᅵᄐ ᅥᄉ ᆺᄋ ᅦ ᅳᄅ ᅩ Naver Sentiment Movie Corpus (NSMC)ᄅ ᆯ ᅳ ᄉᄋ ᅡ ᆼᅡ ᅭ ᄒᄋ ᆻᄃ ᅧ ᅡ. NSMCᄂ ᆫ ᄌ ᅳ ᆫᄎ ᅥ ᅦ 20ᄆ ᆫᄀ ᅡ ᆫᄋ ᅥ ᅴ ᄂ ᅦᄋ ᅵᄇ ᅥ ᄋ ᆼᄒ ᅧ ᅪ ᄅ ᅵᄇ ᅲ ᄃ ᅦᄋ ᅵᄐ ᅥ ᄉ ᆺᄋ ᅦ ᅳᄅ ᅩ, ᄒ ᆨᄉ ᅡ ᆸᄃ ᅳ ᅦᄋ ᅵᄐ ᅥ 150,000ᄀ ᆫ, ᄀ ᅥ ᆷᄌ ᅥ ᆼ ᅳ ᅦᄋ ᄃ ᅵᅥ ᄐ 50,000ᄀ ᆫᄋ ᅥ ᅳᄅ ᅩᄀ ᅮᄉ ᆼᄃ ᅥ ᅬᄋ ᅥᄋ ᆻᄃ ᅵ ᅡ. ᄆ ᆫᄉ ᅮ ᅥᄂ ᆫᄀ ᅳ ᆼᄌ ᅳ ᆼ (1), ᄇ ᅥ ᅮᄌ ᆼ (0)ᄋ ᅥ ᅳᄅ ᅩᄅ ᅦᄋ ᅵᄇ ᆯᄃ ᅳ ᅬᄋ ᅥᄋ ᆻᄋ ᅵ ᅳᄆ ᅧᄀ ᆼᄌ ᅳ ᆼᄃ ᅥ ᅦᄋ ᅵᄐ ᅥ 10ᄆ ᆫᄀ ᅡ ᆫ, ᄇ ᅥ ᅮᄌ ᆼᄃ ᅥ ᅦᄋ ᅵᄐ ᅥ 10ᄆ ᆫᄀ ᅡ ᆫᄋ ᅥ ᅳᄅ ᅩᄀ ᅮᄉ ᆼᄃ ᅥ ᅬᄋ ᅥᄋ ᆻᄃ ᅵ ᅡ. ᄑ ᆼᄌ ᅧ ᆷ 1∼4ᄌ ᅥ ᆷᄋ ᅥ ᆫᄇ ᅳ ᅮᄌ ᆼ, 9∼10ᄌ ᅥ ᆷᄋ ᅥ ᆫᄀ ᅳ ᆼᄌ ᅳ ᆼᄃ ᅥ ᅦᄋ ᅵᄐ ᅥᄅ ᅩᄇ ᆫᄅ ᅮ ᅲᄃ ᅬ ᆻᄃ ᅥ ᄋ ᅡ. 5∼8ᄋ ᆫᅮ ᅳ ᆼ ᄌᄅ ᆸᄃ ᅵ ᅦᄋ ᅵᄐ ᅥᄅ ᅩᄇ ᆫᄅ ᅮ ᅲᄃ ᅬᄋ ᆻᄀ ᅥ ᅩᄇ ᆫᄉ ᅮ ᆨᄋ ᅥ ᅦᄉ ᅥᄌ ᅦᅬ ᄋᄃ ᅬᄋ ᆻᄃ ᅥ ᅡ. ᆫᄋ ᅥ ᄌ ᅵᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅦᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄀ ᅵᄋ ᅱᄒ ᅡᄋ ᅧᄉ ᅦᄀ ᅨᅬ ᄎᄃ ᅢᄌ ᅵᄃ ᅩᄉ ᅥᄇ ᅵᄉ ᅳᄋ ᆫᄀ ᅵ ᅮᄀ ᆯᄆ ᅳ ᆸᄋ ᅢ ᅦᄉ ᅥᄌ ᅵᄋ ᆨᄅ ᅧ ᅵᄇ ᅲᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄉ ᅳ ᅮᄌ ᆸᄒ ᅵ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄒ ᆫ ᅡ ᆨᄀ ᅮ ᄀ ᆫᅪ ᅪ ᆼ ᄀᄎ ᆼᄋ ᅥ ᅴᄎ ᅮᄎ ᆫᄌ ᅥ ᅵᄋ ᆨᅧ ᅧ ᆼ ᄆᄉ ᅩᄅ ᆯᄑ ᅳ ᅡᄋ ᅵᄊ ᆫ (Python)ᄀ ᅥ ᅵᄇ ᆫᄋ ᅡ ᅴᄏ ᅳᄅ ᆯᄅ ᅩ ᅥᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄎ ᆼ 34ᄀ ᅩ ᆺᄋ ᅩ ᅴᄒ ᆫᄀ ᅡ ᆨᄆ ᅮ ᆼᄉ ᅧ ᅩᄋ ᅦᄃ ᅢᄒ ᆫᄌ ᅡ ᅵ ᆨ ᄅ ᅧ ᄋ ᅵᄇ ᅲᅪ ᄋ ᄑ ᆼᄌ ᅧ ᆷᄋ ᅥ ᆯ ᄉ ᅳ ᅮᄌ ᆸ ᄒ ᅵ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄋ ᅵᄅ ᇂᄀ ᅥ ᅦ ᄉ ᆫᅥ ᅥ ᆼ ᄌᄃ ᆫ ᄒ ᅬ ᆫᄀ ᅡ ᆨᄆ ᅮ ᆼᄉ ᅧ ᅩᄋ ᅴ ᄌ ᅵᄋ ᆨᄅ ᅧ ᅵᄇ ᅲᄅ ᅩ ᄎ ᆼ 90,542ᄀ ᅩ ᅢᄀ ᅡ ᄉ ᅮᄌ ᆸᄃ ᅵ ᅬᄋ ᆻᄀ ᅥ ᅩ ᄋ ᅵ ᆼ ᄋ ᅮ ᄌ ᅬᄀ ᆨᄋ ᅮ ᅥᄅ ᅩ ᄌ ᆨᄉ ᅡ ᆼᄃ ᅥ ᆫ 32,178ᄀ ᅬ ᆫ, ᄒ ᅥ ᆫᄃ ᅡ ᆫᄋ ᅡ ᅥᄅ ᅩ ᄋ ᅵᄅ ᅮᄋ ᅥᄌ ᆫ 14,755 ᄀ ᅵ ᆫ, ᄀ ᅥ ᆷᄎ ᅡ ᆫᄆ ᅥ ᆫᄒ ᅮ ᅪᄆ ᅡᄋ ᆯ ᄌ ᅳ ᅵᄋ ᆨᄅ ᅧ ᅵᄇ ᅲ 2,113ᄀ ᆫᄋ ᅥ ᆯ ᄌ ᅳ ᅦᅬ ᄋ ᆫ 41,496ᄀ ᅡ ᄒ ᆫᄋ ᅥ ᆯᄒ ᅳ ᆨᄃ ᅬ ᆨᄒ ᅳ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄌ ᆫᄎ ᅥ ᅦ 41,496ᄀ ᆫᄌ ᅥ ᆼᄀ ᅮ ᆼᄌ ᅳ ᆼ 5,000ᄀ ᅥ ᆫ, ᄇ ᅥ ᅮᄌ ᆼ 5,000ᄀ ᅥ ᆫᄎ ᅥ ᆼ 10,000ᄀ ᅩ ᆫᄋ ᅥ ᅴᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄌ ᅳ ᆫ ᅥ ᅵᄒ ᄋ ᆨᅳ ᅡ ᆸ ᄉᄋ ᅦᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄀ ᅵᄋ ᅱᄒ ᅡᄋ ᅧᄆ ᅮᄌ ᆨᄋ ᅡ ᅱᄅ ᅩᄎ ᅮᄎ ᆯᄒ ᅮ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄋ ᅵᄌ ᆼ 80%ᄋ ᅮ ᆫ 8,000ᄀ ᅵ ᆫᄋ ᅥ ᅴᄃ ᅦᄋ ᅵᄐ ᅥᄂ ᆫᄌ ᅳ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅦᄉ ᅡᄋ ᆼ ᅭ ᅡᄀ ᄒ ᅩᅡ ᄂᄆ ᅥᄌ ᅵ 20%ᄋ ᅦᄉ ᆨᄒ ᅩ ᅡᄂ ᆫ 1,000ᄀ ᅳ ᆫᄋ ᅥ ᅴᄃ ᅦᄋ ᅵᄐ ᅥᄂ ᆫᄆ ᅳ ᅩᄃ ᆯᄀ ᅦ ᆷᄌ ᅥ ᆼᄋ ᅳ ᅦᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄇ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄒ ᆨᄃ ᅬ ᆨᄃ ᅳ ᆫᄃ ᅬ ᅦᄋ ᅵᄐ ᅥᄂ ᆫ ᅳ 1∼3ᄋ ᅵ ᆫᄃ ᅦᄋ ᅵᄐ ᅥᄂ ᆫᄇ ᅳ ᅮᄌ ᆼ (0), ᄑ ᅥ ᆼᄌ ᅧ ᆷᄋ ᅥ ᅵ 4∼5ᄋ ᆫᄃ ᅵ ᅦᄋ ᅵᄐ ᅥᄂ ᆫᄀ ᅳ ᆼᄌ ᅳ ᆼ (1)ᄋ ᅥ ᅳᄅ ᅩᄅ ᅦᄋ ᅵᄇ ᆯᄒ ᅳ ᅡᄋ ᆻᄃ ᅧ ᅡ. 3.2. 전이학습을 위한 CNN모델 생성 3.2.1. 데이터 전처리 NSMC ᄃ ᅦᄋ ᅵᄐ ᅥᄋ ᅪᄌ ᅵᄋ ᆨᄅ ᅧ ᅵᄇ ᅲᄃ ᅦᄋ ᅵᄐ ᅥᄂ ᆫᄆ ᅳ ᅩᄇ ᅡᄋ ᆯᄅ ᅵ ᅩᄌ ᆨᄉ ᅡ ᆼᄃ ᅥ ᆫᄀ ᅬ ᆼᄋ ᅧ ᅮᄀ ᅡᄆ ᆭᄋ ᅡ ᅡᄋ ᅩᄐ ᅡᄂ ᅡᄉ ᆫᄌ ᅵ ᅩᄋ ᅥᄃ ᆼᄋ ᅳ ᅵᄆ ᆭᄋ ᅡ ᅵᄑ ᅩᄒ ᆷᄃ ᅡ ᅬᄋ ᅥ ᄋᄃ ᆻ ᅵ ᅡ. ᄄ ᅡᄅ ᅡᄉ ᅥᄌ ᆼᄒ ᅥ ᆨᄃ ᅪ ᅩᄒ ᆼᅡ ᅣ ᆼ ᄉᄋ ᆯᄋ ᅳ ᅱᄒ ᅢᄉ ᅥᄑ ᅡᄋ ᅵᄊ ᆫᄀ ᅥ ᅵᄇ ᆫᄋ ᅡ ᅴᄆ ᆽᄎ ᅡ ᆷᄇ ᅮ ᆸᄀ ᅥ ᅭᄌ ᆼᄀ ᅥ ᅵᄋ ᆫ py-hanspellᄋ ᅵ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄉ ᅮᄌ ᆸᄃ ᅵ ᆫᄃ ᅬ ᅦ ᅵᄐ ᄋ ᅥᅴ ᄋᄆ ᆽᄎ ᅡ ᆷᄇ ᅮ ᆸᄀ ᅥ ᅪᄄ ᅴᄋ ᅥᄊ ᅳᄀ ᅵᄅ ᆯᄇ ᅳ ᅩᄌ ᆼᄒ ᅥ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄒ ᆼᄐ ᅧ ᅢᄉ ᅩᄇ ᆨᄐ ᅦ ᅥᄅ ᆯᄎ ᅳ ᅮᄎ ᆯᄒ ᅮ ᅡᄀ ᅵᄋ ᅱᄒ ᅢ OKT (Open Korean Text) ᄒ ᆼ ᅧ ᅢᄉ ᄐ ᅩᄇ ᆫᄉ ᅮ ᆨᄀ ᅥ ᅵᄅ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄃ ᆫᄋ ᅡ ᅥᄋ ᅴᄐ ᅩᄏ ᆫᄒ ᅳ ᅪᄀ ᅡᄋ ᅡᄂ ᆫᄒ ᅵ ᆼᄐ ᅧ ᅢᄉ ᅩ (Morpheme) ᄃ ᆫᄋ ᅡ ᅱᄅ ᅩᄒ ᆼᄐ ᅧ ᅢᄉ ᅩᄐ ᅩᄏ ᆫᄒ ᅳ ᅪ (Morpheme tokenization)ᄅ ᆯᄉ ᅳ ᅮᄒ ᆼᄒ ᅢ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄑ ᅡᄋ ᅵᄊ ᆫᄋ ᅥ ᅴ Gensim ᄅ ᅡᄋ ᅵᄇ ᅳᄅ ᅥᄅ ᅵᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧ Word2Vec ᄆ ᅩᄃ ᆯᄌ ᅦ ᆼ CBOWᄋ ᅮ ᆯᄌ ᅳ ᆨ ᅥ ᆼᄒ ᅭ ᄋ ᅡᄋ ᅧ CNNᄆ ᅩᄃ ᆯᄋ ᅦ ᅴᄌ ᆼᄒ ᅥ ᆨᄃ ᅪ ᅩᄅ ᆯᄇ ᅳ ᅵᄀ ᅭᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ. 3.2.2. 대량 데이터 셋을 이용한 CNN 모델생성 ᅢᄅ ᄃ ᆼᄃ ᅣ ᅦᄋ ᅵᄐ ᅥᄉ ᆺᄋ ᅦ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄒ ᆨᄉ ᅡ ᆸᄒ ᅳ ᆯᄃ ᅡ ᅵ ᆸᄅ ᅥᄂ ᆼᄆ ᅵ ᅩᄃ ᆯᄅ ᅦ ᅩᄂ ᆫ Kim (2014)ᄋ ᅳ ᅴᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄉ ᅩᄀ ᅢᄃ ᆫᄆ ᅬ ᅩᄃ ᆯᄌ ᅦ ᆼᄃ ᅮ ᅡᄋ ᆼᅡ ᅣ ᆫ ᄒ ᄃᄋ ᅦ ᅵᅥ ᄐᄉ ᆺᄋ ᅦ ᅦᄃ ᅢᄒ ᅢᄌ ᆫᄎ ᅥ ᅦᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄉ ᆼᄂ ᅥ ᆼᄋ ᅳ ᅵᄋ ᅮᄉ ᅮᄒ ᆻᅥ ᅢ ᆫ ᄃ CNN-non-static ᄆ ᅩᄃ ᆯᄋ ᅦ ᆯᄎ ᅳ ᆷᄀ ᅡ ᅩᄒ ᅡᄋ ᅧᄉ ᆼᅥ ᅢ ᆼ ᄉᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ. CNN ᄆ ᅩ ᆯᄋ ᅦ ᄃ ᆫᄉ ᅳ ᆷᄎ ᅵ ᆼᄉ ᅳ ᆫᅧ ᅵ ᆼ ᄀᄆ ᆼᄋ ᅡ ᅳᄅ ᅩᄒ ᆸᅥ ᅡ ᆼ ᄉᄀ ᆸᄎ ᅩ ᆼ (Convolution layer), ᄑ ᅳ ᆯᄅ ᅮ ᆼᄎ ᅵ ᆼ (Pooling layer), ᄋ ᅳ ᆫᄌ ᅪ ᆫᄋ ᅥ ᆫᅧ ᅧ ᆯ ᄀᄎ ᆼ (Fully conᅳ nected layer)ᄅ ᅩᄀ ᅮᄉ ᆼᄃ ᅥ ᅬᄋ ᅥᄋ ᆻᄃ ᅵ ᅡ. ᄒ ᆸᅥ ᅡ ᆼ ᄉᄀ ᆸᄎ ᅩ ᆼᄀ ᅳ ᅪᄑ ᆯᄅ ᅮ ᆼᄎ ᅵ ᆼᄋ ᅳ ᅦᄉ ᅥᄋ ᆸᄅ ᅵ ᆨᄃ ᅧ ᅦᄋ ᅵᄐ ᅥᄋ ᅴᄐ ᆨᄌ ᅳ ᆼ (Feature)ᄋ ᅵ ᆯᄎ ᅳ ᅮᄎ ᆯᄒ ᅮ ᅡᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅵ CNN ᅩ ᄆᄃ ᆯᄋ ᅦ ᅴᄀ ᅡᄌ ᆼᄏ ᅡ ᆫᄐ ᅳ ᆨᄌ ᅳ ᆼᄋ ᅵ ᅵᄃ ᅡ. ᄉ ᅡᄌ ᆫᄒ ᅥ ᆨᄉ ᅡ ᆸ (Pretrained)ᄋ ᅳ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄀ ᅩᄎ ᅡᄋ ᆫᄋ ᅯ ᅴᄃ ᅦᄋ ᅵᄐ ᅥᄋ ᅦᄉ ᅥᄐ ᆨᄌ ᅳ ᆼᄆ ᅵ ᆫᄋ ᅡ ᆯᄎ ᅳ ᅮᄎ ᆯᄒ ᅮ ᅡ ᅧ ᄌ ᄋ ᅥᅡ ᄎᄋ ᆫᄋ ᅯ ᅴ ᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᆯ ᄋ ᅳ ᆮᄋ ᅥ ᆯ ᄉ ᅳ ᅮ ᄋ ᆻᄀ ᅵ ᅩ, ᄋ ᅵᄅ ᆯ ᄋ ᅳ ᆫᄌ ᅪ ᆫᅧ ᅥ ᆫ 여 ᆯ ᄀᄎ ᆼᄋ ᅳ ᅦᄉ ᅥ ᄇ ᆫᄅ ᅮ ᅲᄒ ᅡᄋ ᅧ ᄄ ᅱᄋ ᅥᄂ ᆫ ᄉ ᅡ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋ ᄇ ᅩᄋ ᅧᄌ ᆫᄃ ᅮ ᅡ. Figure 3.1ᄋ ᆫᄋ ᅳ ᆷᄇ ᅵ ᅦᄃ ᆼᄃ ᅵ ᆫᄃ ᅬ ᆫᄋ ᅡ ᅥᄅ ᆯ 2ᄎ ᅳ ᅡᄋ ᆫᄒ ᅯ ᆼᅧ ᅢ ᆯ ᄅᄅ ᅩᄀ ᅮᄎ ᆨᄒ ᅮ ᅡᄀ ᅩᄀ ᆷᄉ ᅡ ᆼᄋ ᅥ ᆯᅮ ᅳ ᆫ ᄇᄅ ᅲᄒ ᅡᄂ ᆫᄒ ᅳ ᆸᅥ ᅡ ᆼ ᄉᄀ ᆸᄉ ᅩ ᆫᅧ ᅵ ᆼ ᄀᄆ ᆼᄋ ᅡ ᅴᄀ ᅮᄌ ᅩᄅ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᅢᄀ ᅩᄋ ᆻ ᅵ ᅡ. ᅵ ᄃ ᄋᄆ ᅩᄃ ᆯᄋ ᅦ ᆫᄒ ᅳ ᆫᄀ ᅡ ᅢᄒ ᆸᅥ ᅡ ᆼ ᄉᄀ ᆸᄎ ᅩ ᆼᄀ ᅳ ᅪᄒ ᅡᄂ ᅡᄋ ᅴᄋ ᆫᄌ ᅪ ᆫᅧ ᅥ ᆫ 여 ᆯ ᄀᄎ ᆼᄋ ᅳ ᅳᄅ ᅩᄀ ᅮᄉ ᆼᄃ ᅥ ᅬᄆ ᅧ, ᄒ ᆸᅥ ᅡ ᆼ ᄉᄀ ᆸᄎ ᅩ ᆼᄋ ᅳ ᅦᄉ ᅥᄂ ᆫᄒ ᅳ ᆸᅥ ᅡ ᆼ ᄉᄀ ᆸᄋ ᅩ ᆫᄉ ᅧ ᆫᄀ ᅡ ᅪᄆ ᆨᄉ ᅢ ᅳ ᆯᄅ ᅮ ᄑ ᆼᄋ ᅵ ᆫᄉ ᅧ ᆫᄋ ᅡ ᅵᄎ ᅡᄅ ᅨᄃ ᅢᄅ ᅩᄉ ᅮᄒ ᆼᄃ ᅢ ᅬᄋ ᅥᄋ ᆫᄌ ᅪ ᆫᄋ ᅥ ᆫᅧ ᅧ ᆯ ᄀᄎ ᆼᄋ ᅳ ᅴᄋ ᅵᄒ ᅮᄋ ᅦᄂ ᆫ Softmax ᄋ ᅳ ᆫᄉ ᅧ ᆫᄋ ᅡ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄎ ᅬᄌ ᆼᄌ ᅩ ᆨᄋ ᅥ ᆫᄀ ᅵ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄋ ᅥ ᆯᄉ ᅳ ᅮ ᆼᄒ ᅢ ᄒ ᆫᅡ ᅡ ᄃ. CNN ᄆ ᅩᄃ ᆯᄋ ᅦ ᅴᄋ ᆸᄅ ᅵ ᆨᄃ ᅧ ᅦᄋ ᅵᄐ ᅥᄂ ᆫᄐ ᅳ ᆨᄉ ᅦ ᅳᄐ ᅳᄃ ᅦᄋ ᅵᄐ ᅥᄋ ᅦᄉ ᅥᄋ ᅯᄃ ᅳᄋ ᆷᄇ ᅵ ᅦᄃ ᆼᄋ ᅵ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄋ ᆮᄋ ᅥ ᆫᄆ ᅳ ᆫᄌ ᅮ ᆼᅡ ᅡ ᆫ ᄃᄋ ᅱᄋ ᅴ 2ᄎ ᅡᄋ ᆫᄒ ᅯ ᆼ ᅢ ᆯᄋ ᅧ ᄅ ᅵᄃ ᅡ. ᄋ ᅯᄃ ᅳᄋ ᆷᄇ ᅵ ᅦᄃ ᆼᄀ ᅵ ᅪᄌ ᆼᄋ ᅥ ᆯᄀ ᅳ ᅥᄎ ᅧ 2ᄎ ᅡᄋ ᆫᄒ ᅯ ᆼᅧ ᅢ ᆯ ᄅᄅ ᅩᄀ ᅮᄉ ᆼᄃ ᅥ ᆫᄋ ᅬ ᆸᄅ ᅵ ᆨᄃ ᅧ ᅦᄋ ᅵᄐ ᅥᄀ ᅡᄌ ᆫᄇ ᅮ ᅵᅬ ᄃᄆ ᆫᄑ ᅧ ᆯᄐ ᅵ ᅥ (Filter)ᄅ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄋ ᆰ ᅵ ᅥᄂ ᄋ ᅢᅧ ᄅᄀ ᅡᄆ ᅧᄋ ᆸᄅ ᅵ ᆨᄃ ᅧ ᅦᄋ ᅵᄐ ᅥᅪ ᄋᄑ ᆯᄐ ᅵ ᅥᅪ ᄋᄋ ᅴᄒ ᆸᅥ ᅡ ᆼ ᄉᄀ ᆸᄋ ᅩ ᆫᄉ ᅧ ᆫᄋ ᅡ ᅳᄅ ᅩᄐ ᆨᄌ ᅳ ᆼᄌ ᅵ ᅵᄃ ᅩ (Feature map)ᄅ ᆯᄉ ᅳ ᆼᅥ ᅢ ᆼ ᄉᄒ ᆫᄃ ᅡ ᅡ. ᄑ ᆯᄐ ᅵ ᅥᄏ ᅳ ᅵᄂ ᄀ ᆫᄒ ᅳ ᆸᄉ ᅡ ᆼᄀ ᅥ ᆸᄎ ᅩ ᆼᄋ ᅳ ᅦᄉ ᅥᄋ ᆫᄉ ᅧ ᆫᄋ ᅡ ᆯᄎ ᅳ ᅥᄅ ᅵᄒ ᆯᄄ ᅡ ᅢ, ᄒ ᆫᅥ ᅡ ᆫ ᄇᄋ ᅦᄎ ᅥᄅ ᅵᄒ ᆯᄉ ᅡ ᅮᄋ ᆻᄂ ᅵ ᆫᄀ ᅳ ᆫᄌ ᅳ ᆸᄃ ᅥ ᅦᄋ ᅵᄐ ᅥᄌ ᆼᄇ ᅥ ᅩᄋ ᅴᄏ ᅳᄀ ᅵᄅ ᅡᄀ ᅩᄇ ᆯᄉ ᅩ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᅡᄌ ᄉ ᆫᅦ ᅥ ᄋᄌ ᆼᄋ ᅥ ᅴᄃ ᆫᄋ ᅬ ᆸᄅ ᅵ ᆨᄃ ᅧ ᅦᄋ ᅵᄐ ᅥᄋ ᅴᄏ ᅳᄀ ᅵᄀ ᅡᄀ ᆯᅥ ᅧ ᆼ ᄌᄃ ᅬᄆ ᆫ, ᄒ ᅧ ᆸᅥ ᅡ ᆼ ᄉᄀ ᆸᄎ ᅩ ᆼᄑ ᅳ ᆯᄐ ᅵ ᅥᄋ ᅴᄎ ᅬᄌ ᆨᄋ ᅥ ᅴᄏ ᅳᄀ ᅵᄂ ᆫᄉ ᅳ ᆯᄒ ᅵ ᆷᄌ ᅥ ᆨᄋ ᅥ ᅳᄅ ᅩᄀ ᆯᄌ ᅧ ᆼᄒ ᅥ ᅡᄂ ᆫᄃ ᅳ ᅦ.
(5) A sentiment analysis model for small-scale unstructured policy data using transfer learning. 409. ᄇᄋ ᆫ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫ Table 3.1ᄋ ᅳ ᅦᄉ ᅥᄇ ᅩᄃ ᆺᄋ ᅳ ᅵ NSMC ᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᅩᄑ ᆯᄐ ᅵ ᅥᄏ ᅳᄀ ᅵᄅ ᆯ (3,3,3)ᄋ ᅳ ᅦᄉ ᅥ (5,5,5)ᄁ ᅡᄌ ᅵᄇ ᅵᄀ ᅭᄒ ᅡᄋ ᅧᄇ ᆫ ᅮ ᅲᄌ ᄅ ᆼᄒ ᅥ ᆨᄃ ᅪ ᅩᄀ ᅡᄂ ᇁᄋ ᅩ ᆫ (4,3,4)ᄅ ᅳ ᆯᄎ ᅳ ᅬᄌ ᆼᄋ ᅩ ᅳᄅ ᅩᄀ ᆯᅥ ᅧ ᆼ ᄌᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ (Zeilerᄋ ᅪ Fergus, 2014).. Figure 3.1 CNN model structure. Table 3.1 CNN model details Category Model hyperparameters. Training parameters Word2Vec parameters. Item Embedding dimension Filter sizes Number of filters for each filter size Dropout probabilities Hidden dimension Batch size Number of epochs MinimumWord count (min count) Context (window) Vector dimension (size). Value 150 (4, 3, 4) 50 (0.5, 0.8) 50 64 100 (1, 3, 5) 5 150. 3.3. 전이학습 ᄇᄋ ᆫ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄌ ᅦᄋ ᆫᄒ ᅡ ᅡᄂ ᆫᄌ ᅳ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᆫ Figure 3.2ᄋ ᅳ ᅦᄉ ᅥᄇ ᅩᄃ ᆺᄋ ᅳ ᅵᄂ ᅦᄋ ᅵᄇ ᅥᄋ ᅦᄉ ᅥᄌ ᅦᄀ ᆼᄒ ᅩ ᅡᄂ ᆫᄃ ᅳ ᅢᄅ ᆼᄋ ᅣ ᅴᄋ ᆼᄒ ᅧ ᅪᄅ ᅵᄇ ᅲᄃ ᅦᄋ ᅵ ᅥ NSMC 20ᄆ ᄐ ᆫᄀ ᅡ ᆫᄋ ᅥ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄃ ᆸᄅ ᅵ ᅥᄂ ᆼᄆ ᅵ ᅩᄃ ᆯᄋ ᅦ ᆯᄉ ᅳ ᆼᅥ ᅢ ᆼ ᄉᄒ ᆫᄒ ᅡ ᅮᄉ ᆼᄃ ᅡ ᅢᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄌ ᆨᄋ ᅥ ᆫᄉ ᅳ ᅮᄋ ᅴᄌ ᅵᄋ ᆨᄅ ᅧ ᅵᄇ ᅲᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄌ ᅳ ᆫᄋ ᅥ ᅵ ᆨᄉ ᅡ ᄒ ᆸᅵ ᅳ ᄉᄏ ᅧᄆ ᅩᄃ ᆯᄋ ᅦ ᅴᄀ ᅡᄌ ᆼᄎ ᅮ ᅵᄅ ᆯᄇ ᅳ ᆫᄀ ᅧ ᆼᄒ ᅧ ᅡᄂ ᆫᄆ ᅳ ᅩᄃ ᆯᄋ ᅦ ᅵᄃ ᅡ. 3.2ᄌ ᆯᄋ ᅥ ᅦᄉ ᅥᄉ ᅡᄌ ᆫᄒ ᅥ ᆫᄅ ᅮ ᆫᄃ ᅧ ᆫ CNN ᄆ ᅬ ᅩᄃ ᆯᄋ ᅦ ᆯᄅ ᅳ ᅩᄃ ᅳᄒ ᅡᄀ ᅩᄀ ᅡᄌ ᆼᄎ ᅮ ᅵᄅ ᆯᄎ ᅳ ᅩᄀ ᅵᄒ ᅪᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄀ ᅳᄅ ᅵᄀ ᅩᄌ ᅵᄋ ᆨᄅ ᅧ ᅵᄇ ᅲᄃ ᅦᄋ ᅵᄐ ᅥ 1ᄆ ᆫ ᅡ ᆫᄋ ᅥ ᄀ ᆯᅵ ᅳ ᄋᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄀ ᅡᄌ ᆼᄎ ᅮ ᅵᄀ ᅡᄎ ᅩᄀ ᅵᄒ ᅪᄃ ᆫ CNN ᄆ ᅬ ᅩᄃ ᆯᄋ ᅦ ᆯᄒ ᅳ ᆨᄉ ᅡ ᆸᄒ ᅳ ᅡᄀ ᅩᄌ ᆼᄒ ᅥ ᆨᄃ ᅪ ᅩᄋ ᅪᅡ ᆨ ᄒᄉ ᆸᄉ ᅳ ᅵᄀ ᆫᄋ ᅡ ᆯᄒ ᅳ ᆨᄋ ᅪ ᆫᄒ ᅵ ᅡᄋ ᆻᄃ ᅧ ᅡ..
(6) 410. Soonjae Ahn · Seungeui Ryu · Soongoo Hong. Figure 3.2 Transfer learning sentiment analysis model. 4. 연구결과 서 ᆯ ᅵ ᆷ ᄒᄋ ᆫ window10 ᄋ ᅳ ᆫᄋ ᅮ ᆼᄎ ᅧ ᅦᄌ ᅦᄋ ᅦᄉ ᅥ Tensorflow 2.0 ᄋ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄌ ᆫᄒ ᅵ ᆼᄃ ᅢ ᅬᄋ ᆻᄋ ᅥ ᅳᄆ ᅧ, ᄃ ᆸᄅ ᅵ ᅥᄂ ᆼᄆ ᅵ ᅩᄃ ᆯᄋ ᅦ ᅴᄒ ᅭᄋ ᆯᄌ ᅲ ᆨᄋ ᅥ ᆫ ᅵ ᆨᄉ ᅡ ᄒ ᆸᅳ ᅳ ᆯ ᄋᄋ ᅱᄒ ᅢ RTX2080 GPUᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. 4.1. 전이학습 여부에 따른 정확도 비교 ᅮᄌ ᄉ ᆸᄃ ᅵ ᆫ ᄃ ᅬ ᅦᄋ ᅵᄐ ᅥᄅ ᆯ ᄇ ᅳ ᅡᄐ ᆼᄋ ᅡ ᅳᄅ ᅩ ᄒ ᆨᄉ ᅡ ᆸᄀ ᅳ ᆯᄀ ᅧ ᅪᄂ ᆫ Table 4.1ᄀ ᅳ ᅪ ᄀ ᇀᄃ ᅡ ᅡ. ᄌ ᅵᄋ ᆨᄅ ᅧ ᅵᄇ ᅲ 4ᄆ ᆫ ᄀ ᅡ ᆫᄋ ᅥ ᅴ CNN ᄒ ᆨᄉ ᅡ ᆸᄀ ᅳ ᆯᄀ ᅧ ᅪᄋ ᅪ 1ᄆ ᆫ ᄀ ᅡ ᆫᄋ ᅥ ᅴ ᅡ ᆨ ᄒᄉ ᆸᄀ ᅳ ᆯᄀ ᅧ ᅪᄅ ᆯ ᄇ ᅳ ᅵᄀ ᅭᄒ ᅡᄆ ᆫ ᄃ ᅧ ᅦᄋ ᅵᄐ ᅥᄋ ᆼᄋ ᅣ ᅵ ᄌ ᆯᄋ ᅮ ᅥᄃ ᆱᄋ ᅳ ᅦ ᄄ ᅡᄅ ᅡ 73.03%ᄋ ᅦᄉ ᅥ 66.71%ᄅ ᅩ ᄌ ᆼᄒ ᅥ ᆨᄃ ᅪ ᅩᄀ ᅡ ᄒ ᅡᄅ ᆨᄒ ᅡ ᅡᄂ ᆫ ᅳ ᆺᄋ ᅥ ᄀ ᆯ ᄒ ᅳ ᆨᄋ ᅪ ᆫᄒ ᅵ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄄ ᅩᄒ ᆫ ᄃ ᅡ ᅢᄅ ᆼᄋ ᅣ ᅴ ᄃ ᅦᄋ ᅵᄐ ᅥᆫ ᄋ ᅵ NSMC ᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᅩ CNN ᄆ ᅩᄃ ᆯᄋ ᅦ ᆯ ᄉ ᅳ ᆼᅥ ᅢ ᆼ ᄉᄒ ᅡᄋ ᆻᄋ ᅧ ᆯ ᄀ ᅳ ᆼᄋ ᅧ ᅮ ᄌ ᆼᄒ ᅥ ᆨᄃ ᅪ ᅩᄀ ᅡ 84.04%ᄅ ᅩ ᄀ ᅡᄌ ᆼ ᄂ ᅡ ᇁᄋ ᅩ ᆻᄃ ᅡ ᅡ. NSMC ᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᅩ ᄒ ᆨᄉ ᅡ ᆸᄆ ᅳ ᅩᄃ ᆯᄋ ᅦ ᆯ ᄆ ᅳ ᆫᄃ ᅡ ᆯᄀ ᅳ ᅩ ᄌ ᅵᄋ ᆨᄅ ᅧ ᅵᄇ ᅲ 1ᄆ ᆫ ᄀ ᅡ ᆫᄋ ᅥ ᆯ ᄌ ᅳ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸ ᄉ ᅳ ᅵᄏ ᆫ ᅧ ᅵ ᆼ ᄀᄋ ᅮ Table 4.1.1ᄋ ᅦᄉ ᅥᄇ ᅩᄃ ᆺᄋ ᅳ ᅵ 76.69%ᄅ ᅩᄉ ᅩᄅ ᆼᄋ ᅣ ᅴᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᅩᄆ ᆫᄒ ᅡ ᆨᄉ ᅡ ᆸᄒ ᅳ ᆫᄀ ᅡ ᆼᄋ ᅧ ᅮᄇ ᅩᄃ ᅡᄌ ᆼᄒ ᅥ ᆨᄃ ᅪ ᅩᄀ ᅡᄂ ᇁᄋ ᅩ ᆻᄋ ᅡ ᅳᄆ ᅧ 4ᄆ ᆫᄀ ᅡ ᆫᄋ ᅥ ᅴᄌ ᅵ ᆨᄅ ᅧ ᄋ ᅵᄇ ᅲᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᅩᄒ ᆨᄉ ᅡ ᆸᄒ ᅳ ᆫᅧ ᅡ ᆼ ᄀᄋ ᅮᄇ ᅩᄃ ᅡᄌ ᆼᄒ ᅥ ᆨᄃ ᅪ ᅩᄀ ᅡᄃ ᅥᄂ ᇁᄋ ᅩ ᆻᄃ ᅡ ᅡ. Table 4.1 Training accuracy Model Local reviews CNN model (40,000 cases) Local reviews CNN model (10,000 cases) NSMC CNN model (200,000 case) NSMC CNN model → Local reviews transfer learning. Accuracy 73.03 % 66.71 % 84.04 % 76.69 %. 4.2. 전이학습 여부에 따른 학습시간 비교 ᄌᄒ ᆼ ᅥ ᆨᄃ ᅪ ᅩᄈ ᆫᄆ ᅮ ᆫᄋ ᅡ ᅡᄂ ᅵᄃ ᅡᄒ ᆨᄉ ᅡ ᆸᄉ ᅳ ᅵᄀ ᆫᄋ ᅡ ᅦᄉ ᅥᄃ ᅩᄎ ᅡᄋ ᅵᄀ ᅡᄇ ᆯᄉ ᅡ ᆼᄒ ᅢ ᅡᄂ ᆫᄌ ᅳ ᅵᄅ ᆯᄀ ᅳ ᆫᄎ ᅪ ᆯᄒ ᅡ ᅡᄋ ᆻᄃ ᅧ ᅡ. Table 4.2ᄋ ᅦᄉ ᅥᅩ ᄇᄃ ᆺᄋ ᅳ ᅵᄌ ᅵᄋ ᆨᄅ ᅧ ᅵ ᅲ 1ᄆ ᄇ ᆫᄀ ᅡ ᆫᄋ ᅥ ᅳᄅ ᅩ CNN ᄒ ᆨᄉ ᅡ ᆸᄒ ᅳ ᆯᄄ ᅡ ᅢᄇ ᅩᄃ ᅡ NSMC ᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᅩᄒ ᆨᄉ ᅡ ᆸᄆ ᅳ ᅩᄃ ᆯᄋ ᅦ ᆯᄆ ᅳ ᆫᄃ ᅡ ᆯᄋ ᅳ ᅥᄂ ᇂᄀ ᅩ ᅩᄌ ᅵᄋ ᆨᄅ ᅧ ᅵᄇ ᅲ 1ᄆ ᆫᅥ ᅡ ᄀᄋ ᆫ ᅳᄅ ᅩᄌ ᆫ ᅥ ᅵᄒ ᄋ ᆨᅳ ᅡ ᆸ 스 ᆯ ᄋᄒ ᆯᄄ ᅡ ᅢ 1 epochᄃ ᆼ 2968msᄋ ᅡ ᅦᄉ ᅥ 1822msᄅ ᅩᄒ ᆨᄉ ᅡ ᆸᄉ ᅳ ᅵᄀ ᆫᄋ ᅡ ᅵᄃ ᆫᄎ ᅡ ᆨᄃ ᅮ ᅬᄋ ᆻᄃ ᅥ ᅡ..
(7) A sentiment analysis model for small-scale unstructured policy data using transfer learning. 411. Table 4.2 Training time Model Local reviews model NSMC CNN model → Local reviews transfer learning. Training time 2968 ms / 1 epoch 1822 ms / 1 epoch. 5. 결론 ᄇᄋ ᆫ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄌ ᅳ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋᄐ ᆼᄒ ᅩ ᅡᄋ ᅧᄉ ᅩᄀ ᅲᄆ ᅩᄃ ᅦᄋ ᅵᄐ ᅥᄋ ᅴᄀ ᆷᄉ ᅡ ᆼᄑ ᅥ ᆼᄀ ᅧ ᅡᄀ ᆯᄀ ᅧ ᅪᄅ ᆯᄒ ᅳ ᆼᅡ ᅣ ᆼ ᄉᄉ ᅵᄏ ᅵᄀ ᅩᄌ ᅡᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄀ ᅵᄌ ᆫᄋ ᅩ ᅴᄃ ᆸ ᅵ ᅥᄂ ᄅ ᆼᅵ ᅵ ᄀᄇ ᆫᄋ ᅡ ᅴᄀ ᆷᄉ ᅡ ᆼᅧ ᅥ ᆼ ᄑᄀ ᅡᄇ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅳᄅ ᅩᄂ ᆫᄃ ᅳ ᅦᄋ ᅵᄐ ᅥᄋ ᆼᄋ ᅣ ᅵᄌ ᆨᄋ ᅥ ᅳᄆ ᆫᄒ ᅧ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅴᄌ ᆼᄒ ᅥ ᆨᄃ ᅪ ᅩᄀ ᅡᄄ ᆯᄋ ᅥ ᅥᄌ ᆫᄃ ᅵ ᅡᄂ ᆫᄒ ᅳ ᆫᄀ ᅡ ᅨᄀ ᅡᄋ ᆻᄋ ᅵ ᆻᄃ ᅥ ᅡ. ᄋ ᅵ ᆯᄋ ᅳ ᄅ ᅱᄒ ᅢᄇ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄂ ᅳ ᅦᄋ ᅵᄇ ᅥᄋ ᆼᄒ ᅧ ᅪᄅ ᅵᄇ ᅲᄅ ᅡᄂ ᆫᄃ ᅳ ᅢᄅ ᆼᄋ ᅣ ᅴᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅡᄋ ᅧᄃ ᆸᄅ ᅵ ᅥᄂ ᆼᄆ ᅵ ᅩᄃ ᆯᄋ ᅦ ᆯᄆ ᅳ ᆫᄃ ᅡ ᆯᄀ ᅳ ᅩᄋ ᅵᄆ ᅩᄃ ᆯᄋ ᅦ ᆯ ᅳ ᅢᄉ ᄌ ᅡᅭ ᆼ ᄋᄒ ᅡᄂ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᆯᄐ ᅳ ᆨᄒ ᅢ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄋ ᅵᄅ ᅥᄒ ᆫᄇ ᅡ ᆼᄇ ᅡ ᆸᄋ ᅥ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅡᄋ ᅧᄆ ᆨᄑ ᅩ ᅭᄅ ᅩᄒ ᅡᄀ ᅩᄋ ᆻᄂ ᅵ ᆫᄌ ᅳ ᅵᄋ ᆨᄅ ᅧ ᅵᄇ ᅲᄀ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄆ ᅥ ᅩᄃ ᆯᄋ ᅦ ᅴᄒ ᆨᄉ ᅡ ᆸ ᅳ ᆯᄒ ᅳ ᄋ ᆼᄉ ᅣ ᆼᄉ ᅡ ᅵᄏ ᆻᄀ ᅧ ᅩᄂ ᇁᄋ ᅩ ᅡᄌ ᆫᅥ ᅵ ᆼ ᄌᄒ ᆨᄃ ᅪ ᅩᄅ ᆯᄒ ᅳ ᆨᄋ ᅪ ᆫᄒ ᅵ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄄ ᅩᄒ ᆫᄆ ᅡ ᅩᄃ ᆯᄋ ᅦ ᅴᄀ ᅡᄌ ᆼᄎ ᅮ ᅵᄆ ᆫᄋ ᅡ ᆯᄇ ᅳ ᆫᅧ ᅧ ᆼ ᄀᄉ ᅵᄏ ᅵᄂ ᆫᄌ ᅳ ᆫᄅ ᅥ ᆨᄋ ᅣ ᆯᄐ ᅳ ᆨᄒ ᅢ ᅡᄋ ᅧᄆ ᅩᄃ ᆯ ᅦ ᆯᄉ ᅳ ᄋ ᆼᅥ ᅢ ᆼ ᄉᄒ ᅡᄂ ᆫᄃ ᅳ ᆫᄀ ᅡ ᅨᄅ ᆯᄀ ᅳ ᆫᄂ ᅥ ᅥᄄ ᅱᄋ ᅥᄒ ᆨᄉ ᅡ ᆸᄉ ᅳ ᅵᄀ ᆫᄋ ᅡ ᆯᄃ ᅳ ᆫᄎ ᅡ ᆨᄒ ᅮ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄇ ᆫᄉ ᅮ ᆨᅧ ᅥ ᆯ ᄀᄀ ᅪᄃ ᅢᄅ ᆼᄋ ᅣ ᅴᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᅩᄒ ᆨᄉ ᅡ ᆸᄆ ᅳ ᅩᄃ ᆯᄋ ᅦ ᆯᄆ ᅳ ᆫᄃ ᅡ ᆯᄀ ᅳ ᅩᄉ ᅩ ᆼᄋ ᅣ ᄅ ᅴᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᅩᄌ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄋ ᅧ ᆯᄄ ᅳ ᅢᄉ ᅩᄅ ᆼᄋ ᅣ ᅴᄃ ᅦᄋ ᅵᄐ ᅥᄆ ᆫᄋ ᅡ ᅳᄅ ᅩᄒ ᆨᄉ ᅡ ᆸᄒ ᅳ ᅡᄋ ᆻᄋ ᅧ ᆯᄄ ᅳ ᅢᄇ ᅩᄃ ᅡᄋ ᆨ 10%ᄋ ᅣ ᅴᄌ ᆼᄒ ᅥ ᆨᄃ ᅪ ᅩᄒ ᆼ ᅣ ᆼᄀ ᅡ ᄉ ᅪ 1epoch ᄃ ᆼᄋ ᅡ ᆨ 1000msᄋ ᅣ ᅴᄒ ᆨᄉ ᅡ ᆸᄉ ᅳ ᅵᄀ ᆫᄃ ᅡ ᆫᄎ ᅡ ᆨᅳ ᅮ ᆯ ᄋᄒ ᆨᄋ ᅪ ᆫᄒ ᅵ ᆯᄉ ᅡ ᅮᄋ ᆻᄋ ᅵ ᆻᄃ ᅥ ᅡ. ᄋ ᅵᄂ ᆫᄌ ᅳ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋᄐ ᆼᄒ ᅩ ᅡᄋ ᅧᄃ ᅦᄋ ᅵᄐ ᅥᄋ ᆼ ᅣ ᅵᄌ ᄋ ᆨᄋ ᅥ ᆫᅮ ᅳ ᆫ ᄇᄋ ᅣᄋ ᅦᄉ ᅥᄃ ᅩᄃ ᆸᄅ ᅵ ᅥᄂ ᆼᄀ ᅵ ᆷᄉ ᅡ ᆼᅧ ᅥ ᆼ ᄑᄀ ᅡᄀ ᅡᄀ ᅡᄂ ᆼᄒ ᅳ ᅡᄃ ᅡᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᆯᄋ ᅳ ᅴᄆ ᅵᄒ ᆫᄃ ᅡ ᅡ. ᄄ ᅩᄒ ᆫ, ᄀ ᅡ ᅵᄌ ᆫᄉ ᅩ ᆫᄒ ᅥ ᆼᅧ ᅢ ᆫ ᄋᄀ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄋ ᅳ ᆼᄋ ᅧ ᅥᄐ ᆨ ᅦ ᅳᄐ ᄉ ᅳᅦ ᄃᄋ ᅵᄐ ᅥᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄌ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᄀ ᅳ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄋ ᅥ ᆯᄉ ᅳ ᅵᄃ ᅩᄒ ᅡᄋ ᆻᄌ ᅧ ᅵᄆ ᆫᄇ ᅡ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄒ ᅳ ᆫᄀ ᅡ ᆯᄐ ᅳ ᆨᄉ ᅦ ᅳᄐ ᅳᄋ ᅦᄉ ᅥᄃ ᅩᄌ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸ ᅳ ᆷᄉ ᅡ ᄀ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄋ ᅥ ᅵᄀ ᅡᄂ ᆼᄒ ᅳ ᅡᄃ ᅡᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᆯᄌ ᅳ ᆼᄆ ᅳ ᆼᄒ ᅧ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᆫᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄋ ᅴᄀ ᆼᄒ ᅩ ᆫᄃ ᅥ ᅩᄅ ᅩᄒ ᆨᄉ ᅡ ᆯᄌ ᅮ ᆨᄋ ᅥ ᅳᄅ ᅩᄂ ᆫᄒ ᅳ ᆫᄀ ᅡ ᆯᄐ ᅳ ᆨᄉ ᅦ ᅳᄐ ᅳᄀ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄋ ᅥ ᅦᄌ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋᄎ ᅥᄋ ᆷᄋ ᅳ ᅳᄅ ᅩᄌ ᆨᄋ ᅥ ᆼᄒ ᅭ ᅡᄋ ᅧᄒ ᆼᄒ ᅣ ᅮᄉ ᅩᄀ ᅲ ᅩᄃ ᄆ ᅦᅵ ᄋᄐ ᅥᄋ ᅴᄀ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄋ ᅥ ᆫᄀ ᅧ ᅮᄋ ᅦᄒ ᆯᄋ ᅪ ᆼᄒ ᅭ ᆯᄉ ᅡ ᅮᄋ ᆻᄂ ᅵ ᆫᄋ ᅳ ᅵᄅ ᆫᄌ ᅩ ᆨᄀ ᅥ ᅵᄇ ᆫᄋ ᅡ ᆯᄌ ᅳ ᅦᄀ ᆼᄒ ᅩ ᅡᄋ ᆻᄃ ᅧ ᅡᄂ ᆫᄌ ᅳ ᆷᄋ ᅥ ᅵᄃ ᅡ. ᄉ ᆯᄆ ᅵ ᅮᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄂ ᆫᄃ ᅳ ᅦᄋ ᅵ ᅥᄀ ᄐ ᅡᅮ ᄇᄌ ᆨᄒ ᅩ ᅡᄋ ᅧᄉ ᅵᄃ ᅩᄒ ᅡᄀ ᅵᄋ ᅥᄅ ᅧᄋ ᆻᄃ ᅯ ᆫᄌ ᅥ ᆼᄎ ᅥ ᆨᄇ ᅢ ᆫᄋ ᅮ ᅣᄋ ᅴᄀ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄋ ᅥ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄌ ᆼᄇ ᅥ ᅮᄉ ᅡᄋ ᆸᄋ ᅥ ᅴᄉ ᆼᄀ ᅥ ᆼᄋ ᅩ ᅧᄇ ᅮᄅ ᆯᄑ ᅳ ᆫᄇ ᅡ ᆯᄒ ᅧ ᆯᄉ ᅡ ᅮᄋ ᆻᄂ ᅵ ᆫ ᅳ ᅵᄎ ᄀ ᅩᅡ ᄌᄅ ᅭᄅ ᅩᄒ ᆯᄋ ᅪ ᆼᄒ ᅭ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᆫᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄀ ᅡᄀ ᅡᄌ ᅵᄂ ᆫᄒ ᅳ ᆫᄀ ᅡ ᅨᄌ ᆷᄀ ᅥ ᅪᅣ ᆼ ᄒᄒ ᅮᄋ ᆫᄀ ᅧ ᅮᅪ ᄀᄌ ᅦᄂ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄃ ᅡ ᅡ. ᆺᄍ ᅥ ᄎ ᅢ, ᄌ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅦᄃ ᅢᄒ ᆫᄉ ᅡ ᅮᄒ ᆨᄆ ᅡ ᆾᄋ ᅵ ᅵᄅ ᆫᄌ ᅩ ᆨᄋ ᅥ ᆫᄇ ᅵ ᅢᄀ ᆼᄋ ᅧ ᅵᄋ ᅡᄌ ᆨᄌ ᅵ ᆼᄅ ᅥ ᆸᄃ ᅵ ᅬᄌ ᅵᄋ ᆭᄋ ᅡ ᅡᄌ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᆯᄀ ᅳ ᅮᄉ ᆼᄒ ᅥ ᆯᄄ ᅡ ᅢᄉ ᆼᄃ ᅡ ᆼᄒ ᅡ ᅵᄀ ᆼ ᅧ ᆷᄌ ᅥ ᄒ ᆨᅳ ᅥ ᄋᄅ ᅩᄌ ᆸᄀ ᅥ ᆫᄒ ᅳ ᆯᄉ ᅡ ᅮᄇ ᆩᄋ ᅡ ᅦᄋ ᆹᄃ ᅥ ᅡ. ᄀ ᆼᄒ ᅧ ᆷᄌ ᅥ ᆨᄋ ᅥ ᆫᅥ ᅵ ᆸ ᄌᄀ ᆫᅳ ᅳ ᆯ ᄋᄐ ᆼᄒ ᅩ ᅢᄉ ᅥᄃ ᅥᄌ ᇂᄋ ᅩ ᆫᄀ ᅳ ᆯᄀ ᅧ ᅪᄅ ᆯᄋ ᅳ ᆮᄋ ᅥ ᆯᄉ ᅳ ᅮᄋ ᆻᄌ ᅵ ᅵᄆ ᆫᄌ ᅡ ᆼᄒ ᅥ ᆨᄒ ᅪ ᆫᄋ ᅡ ᅵᄅ ᆫᄌ ᅩ ᆨᄋ ᅥ ᆫ ᅵ ᅢᄋ ᄂ ᆼᅵ ᅭ ᄋᄌ ᆼᄅ ᅥ ᆸᄃ ᅵ ᅬᄋ ᅥᄋ ᆻᄃ ᅵ ᅡᄆ ᆫᄉ ᅧ ᅵᄒ ᆼᄎ ᅢ ᆨᄋ ᅡ ᅩᄋ ᆹᄋ ᅥ ᅵᄌ ᇂᄋ ᅩ ᆫᄉ ᅳ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋᄀ ᅵᄃ ᅢᄒ ᆯᄉ ᅡ ᅮᄋ ᆻᄋ ᅵ ᆯᄀ ᅳ ᆺᄋ ᅥ ᅵᄃ ᅡ. ᆯᄍ ᅮ ᄃ ᅢ, ᄇ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄋ ᆮᄋ ᅥ ᆫᄀ ᅳ ᆯᄀ ᅧ ᅪᄂ ᆫᄂ ᅳ ᅦᄋ ᅵᄇ ᅥᄋ ᆼᄒ ᅧ ᅪᄅ ᅵᄇ ᅲᄅ ᅡᄂ ᆫᄆ ᅳ ᅢᄋ ᅮᄆ ᆭᄋ ᅡ ᆫᄃ ᅳ ᅦᄋ ᅵᄐ ᅥᄉ ᆺᄋ ᅦ ᆯᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅡᄋ ᅧᄑ ᆼᄇ ᅧ ᆷᄒ ᅥ ᆫ CNN ᅡ ᅩᄃ ᄆ ᆯᅴ ᅦ ᄋᄉ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋᄒ ᆼᅡ ᅣ ᆼ ᄉᄉ ᅵᄏ ᅧᄌ ᆫᄋ ᅥ ᅵᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅴᄀ ᅡᄂ ᆼᄉ ᅳ ᆼᄋ ᅥ ᆯᄒ ᅳ ᆨᄋ ᅪ ᆫᄒ ᅵ ᅡᄋ ᆻᄌ ᅧ ᅵᄆ ᆫᄃ ᅡ ᅥᄇ ᆨᄌ ᅩ ᆸᄒ ᅡ ᆫᄆ ᅡ ᅩᄃ ᆯᄋ ᅦ ᆯᄆ ᅳ ᆫᄃ ᅡ ᆯᄋ ᅳ ᅥᄉ ᆼᄂ ᅥ ᆼᄋ ᅳ ᅴᄇ ᆫᄒ ᅧ ᅪᄅ ᆯᄀ ᅳ ᆫ ᅪ ᆯᅡ ᅡ ᄎ ᆯ ᄒᆯ ᅵᄋ ᄑ ᅭᄀ ᅡᄋ ᆻᄃ ᅵ ᅡ. ᆺᄍ ᅦ ᄉ ᅢ, ᄇ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄌ ᅳ ᅵᄋ ᆨᄅ ᅧ ᅵᄇ ᅲᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅡᄋ ᅧᄉ ᅩᄅ ᆼᄋ ᅣ ᅴᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᅩᄃ ᆸᄅ ᅵ ᅥᄂ ᆼᄀ ᅵ ᆷᄉ ᅡ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆨᄆ ᅥ ᅩᄃ ᆯᄋ ᅦ ᅴᄉ ᆼᄂ ᅥ ᆼᄒ ᅳ ᆼᅡ ᅣ ᆼ ᄉᄋ ᆯ ᅳ ᆨᄋ ᅪ ᄒ ᆫᅡ ᅵ ᄒᄋ ᆻᄌ ᅧ ᅵᄆ ᆫᄉ ᅡ ᆯᄌ ᅵ ᅦᄌ ᆼᅢ ᅥ ᆨ ᄎᄃ ᅦᄋ ᅵᄐ ᅥᄈ ᆫᄆ ᅮ ᆫᄋ ᅡ ᅡᄂ ᅵᄅ ᅡᄃ ᅦᄋ ᅵᄐ ᅥᄋ ᆼᄋ ᅣ ᅵᄌ ᆨᄋ ᅡ ᅡᄃ ᆸᄅ ᅵ ᅥᄂ ᆼᄋ ᅵ ᆯᄐ ᅳ ᆼᄒ ᅩ ᆫᄇ ᅡ ᆫᄅ ᅮ ᅲᄌ ᆨᄋ ᅡ ᆸᄋ ᅥ ᅵᄋ ᅥᄅ ᅧᄋ ᆻᄃ ᅯ ᆫᄇ ᅥ ᆫᄋ ᅮ ᅣ ᅦᄌ ᄋ ᆨᅭ ᅥ ᆼ ᄋᄒ ᆫᄃ ᅡ ᅡᄆ ᆫᄃ ᅧ ᅩᄋ ᆷᄋ ᅮ ᅵᄃ ᆯᄀ ᅬ ᆺᄋ ᅥ ᅳᄅ ᅩᄉ ᅡᄅ ᅭᄃ ᆫᄃ ᅬ ᅡ.. References Alashri, S., Kandala, S. S., Bajaj, V., Ravi, R., Smith, K. L. and Desouza, K. C. (2016). An analysis of sentiments on Facebook during the 2016 U.S. presidential election. Proceeding of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 795-802. Blitzer, J., McDonald, R. and Pereira, F. (2016). Domain adaptation with structural correspondence learning. Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, 120128. Bravo-Marquez, F., Frank, E. and Pfahringer, B. (2016). Building a Twitter opinion lexicon from automatically-annotated tweets. Knowledge-Based Systems, 108, 65-78..
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(10) Journal of the Korean Data & Information Science Society 2020, 31(2), 405–414. http://dx.doi.org/10.7465/jkdi.2020.31.2.405 ᆫᄀ ᅡ ᄒ ᆨᄃ ᅮ ᅦᄋ ᅵᄐ ᅥᄌ ᆼᄇ ᅥ ᅩᅪ ᄀᄒ ᆨᄒ ᅡ ᅬᄌ ᅵ. A sentiment analysis model for small-scale unstructured policy data using transfer learning. †. Soonjae Ahn1 · Seungui Ryu2 · Soongoo Hong3 12. 3. Smart Governance Research Center, Dong-A University Department of Management Information Systems, Dong-A University Received 21 January 2020, revised 3 March 2020, accepted 4 March 2020. Abstract Despite the recent development in big data technologies, the research on sentiment analysis is still facing many limitations due to the lack of unstructured data, including texts, in the policy field. Thus, this study proposes a sentiment analysis model for smallscale unstructured policy data based on transfer learning. As a result, the proposed transfer learning model achieved about 10% better accuracy and a shorter training time of 1000 ms per epoch than the model generated only by using small-scale data. As an academic contribution, this study, which is the first application of transfer learning to Korean text sentiment analysis, provides a theoretical basis for future research on sentiment analysis using small-scale data. For practicality, this study can serve as basic data in determining the success or failure of government projects through sentiment analysis in the policy field, which was difficult to determine previously given the lack of data. the detailed feature of sea/land breeze at each site is closely associated with the local shape of coastline. Keywords: Big data analysis, Deep learning, Policy evaluation, Sentiment analysis, Small-scale data, Transfer learning.. †. This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2018S1A3A2075240). 1 Corresponding author: Ph.D., Smart Governance Research Center, Dong-A University, Busan 49236, Korea. E-mail: asj0502@donga.ac.kr 2 Ph.D., Smart Governance Research Center, Dong-A University, Busan 49236, Korea. 3 Department of Management Information Systems, Dong-A University, Busan 49236, Korea..
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