LSA analyzes relationships between documents and terms by decomposing a document-term matrix using singular value decomposition. This reduces the dimensionality of the document vectors to identify topics in a collection of documents. The example shows LSA applied to a corpus of news articles in Korean, generating 10 topics with the most representative terms for each topic.

1. Latent Semantic Analysis
Yonghee Cheon

2. 1
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배경

3. 3
배경
LSA vs LSI
Latent Semantic Analysis
In Distributional Semantics
Latent Semantic Indexing
In Information Retrieval Tasks
VS

4. 4
Q쿼리 정보알고리즘
배경
Information Retrieval Task
반환
Information Retrieval Task
e.g. 검색엔진

5. 5
Q
배경
Vector Space Model
쿼리와문서사이유사도를측정하고싶다
쿼리 문서
쿼리와문서모두벡터라면코사인,유클리드거리등을이용할수있다
BagofWordsModel

6. 6
배경
Bag of Words Model
1. The exact ordering of the terms in a document is ignored
2. The number of occurrences of each term is material

7. 배경
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Title : SK Telecom Co., LTd.
'SK Telecom Co., Ltd. provides wireless telecommunications in Korea. The
Company is engaged in the commercial development and implementation
of wireless and fixed-line technologies and services, as well as develop its
platforms, including Internet of things (IoT) solutions, lifestyle enhancement
and advanced media. It operates through three segments: cellular
services, which include wireless voice and data transmission services,
sales of wireless devices, IoT solutions platform services and lifestyle
enhancement platform services; fixed-line telecommunication services,
which include fixed-line telephone services, broadband Internet services,
advanced media platform services (including Internet Protocol television
(IPTV)) and business communications services, and other businesses,
which include its commerce business, its hardware business and other
operations. Its brands include SK Telecom, T-Roaming, 7Mobile, B phone,
00700, B tv, Syrup, UO Smart Beam Laser, Astell&Kern and Nate.'
[Document: SK Telecom]
wireless : 4
telecommunication : 2
.
.
.
.
Document-Term Matrix (1)

8. 배경
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Document-Term Matrix (2)

9. 배경
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LSI 개요
DTM의Low-RankApproximation을통해
더작은차원의DocumentVector를만들거나,
Topic을추출하는알고리즘

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SingularVectorDecomposition

11. SingularVectorDecomposition
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행렬 분해: Matrix Decomposition
한 행렬을 서로 다른 행렬의 곱 등으로 분해하는 것What
계산이 더 쉬워지거나, 시간이 줄어들 수 있음
차원을 축소하여 시간, 메모리 효율을 얻을 수 있음
ExpectedEffect

12. SingularVectorDecomposition
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특이값 분해: SVD
- 정사각행렬이 아닌 행렬에도 적용할 수 있음특징
수식
- Singular Value는 내림차순으로 정렬된 대각행렬에 존재,
이 중 상위 몇 개의 값을 사용하느냐를 통해 low-rank approximation 가능
(Truncated SVD)

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LatentSemanticAnalysis

14. LatentSemanticAnalysis
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LSA 개요

15. LatentSemanticAnalysis
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네이버 뉴스 크롤링 데이터

16. LatentSemanticAnalysis
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DTM 만들기

17. LatentSemanticAnalysis
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Tokens Matrix

18. LatentSemanticAnalysis
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DTM 만들기 - 함수

19. LatentSemanticAnalysis
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Token2Idx

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Document-Term Matrix

21. LatentSemanticAnalysis
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SVD (1)

22. LatentSemanticAnalysis
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SVD (2)

23. LatentSemanticAnalysis
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LSA 결과 (count)
Topic 1: [('정규직', 0.44), ('전환', 0.25), ('비정규직', 0.24), ('의원', 0.21), ('청년', 0.16), ('문제', 0.16), ('보안', 0.15), ('국공', 0.15), ('뉴스', 0.15), ('경제', 0.13)]
Topic 2: [('코로나', 0.48), ('경제', 0.34), ('확진', 0.2), ('지역', 0.13), ('미국', 0.1), ('설빙', 0.1), ('세계', 0.09), ('확산', 0.08), ('한국', 0.08), ('상황', 0.08)]
Topic 3: [('의원', 0.39), ('정치', 0.22), ('민주당', 0.2), ('통합', 0.17), ('생각', 0.16), ('장관', 0.16), ('위원장', 0.15), ('국회', 0.15), ('문제', 0.15), ('북한', 0.12)]
Topic 4: [('설빙', 0.83), ('딸기', 0.17), ('디저트', 0.16), ('메뉴', 0.15), ('빙수', 0.12), ('메론', 0.1), ('출시', 0.08), ('인절미', 0.07), ('제공', 0.07), ('카페', 0.06)]
Topic 5: [('경제', 0.42), ('산업', 0.15), ('기업', 0.14), ('정부', 0.12), ('지원', 0.1), ('디지털', 0.09), ('전망', 0.08), ('글로벌', 0.08), ('사회', 0.08), ('위기', 0.08)]
Topic 6: [('보안', 0.2), ('장관', 0.19), ('북한', 0.18), ('노조', 0.17), ('검색', 0.17), ('공사', 0.16), ('정치', 0.15), ('요원', 0.15), ('공항', 0.13), ('노회찬', 0.12)]
Topic 7: [('노회찬', 0.41), ('정치', 0.35), ('뉴스', 0.16), ('지역', 0.12), ('대표', 0.12), ('트위터', 0.11), ('소통', 0.1), ('사람', 0.09), ('토론', 0.09), ('지방', 0.08)]
Topic 8: [('확진', 0.29), ('의원', 0.2), ('지역', 0.18), ('광주', 0.15), ('국회', 0.14), ('산업', 0.14), ('생각', 0.13), ('보안', 0.12), ('부분', 0.11), ('기업', 0.11)]
Topic 9: [('문제', 0.34), ('확진', 0.22), ('생각', 0.2), ('북한', 0.18), ('비정규직', 0.15), ('정부', 0.15), ('부분', 0.14), ('정책', 0.1), ('이야기', 0.1), ('사회', 0.1)]
Topic 10: [('의원', 0.29), ('보안', 0.17), ('미국', 0.16), ('북한', 0.16), ('위원장', 0.15), ('검색', 0.14), ('코로나', 0.12), ('통합', 0.11), ('대통령', 0.09), ('노조', 0.09)]

24. LatentSemanticAnalysis
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LSA 결과 (tf-idf)
Topic1:[('정규직',0.42),('비정규직',0.23),('의원',0.23),('전환',0.22),('청년',0.18),('국공',0.17),('보안',0.15),('일자리',0.13),('공정',0.13),('문제',0.12)]
Topic2:[('설빙',0.77),('디저트',0.18),('메뉴',0.17),('딸기',0.11),('확진',0.1),('빙수',0.1),('인절미',0.09),('경제',0.09),('출시',0.09),('매장',0.08)]
Topic3:[('확진',0.46),('경제',0.16),('환자',0.16),('지역',0.15),('감염',0.15),('미국',0.13),('판정',0.12),('신규',0.12),('발생',0.12),('교회',0.12)]
Topic4:[('확진',0.42),('정규직',0.18),('판정',0.14),('교회',0.13),('감염',0.11),('설빙',0.11),('검사',0.09),('발생',0.09),('비정규직',0.09),('보안',0.09)]
Topic5:[('의원',0.42),('민주당',0.19),('확진',0.16),('통합',0.15),('정치',0.15),('대표',0.15),('국회',0.13),('검찰',0.13),('위원장',0.11),('광주',0.09)]
Topic6:[('미국',0.25),('의원',0.23),('환자',0.22),('신규',0.13),('경제',0.12),('트럼프',0.11),('재개',0.11),('민주당',0.1),('텍사스',0.09),('플로리다주',0.08)]
Topic7:[('검찰',0.29),('회장',0.29),('모빌리티',0.22),('라임',0.21),('수사',0.2),('스타',0.18),('구속',0.17),('장관',0.17),('대표',0.16),('의혹',0.14)]
Topic8:[('국세청',0.39),('홍보',0.33),('대사',0.26),('성실',0.24),('아이유',0.23),('납세자',0.18),('이서진',0.17),('납세',0.16),('대통령',0.15),('환자',0.14)]
Topic9:[('국세청',0.23),('홍보',0.19),('경제',0.17),('대사',0.16),('의원',0.15),('아이유',0.14),('전망',0.14),('성실',0.14),('회장',0.13),('납세자',0.11)]
Topic10:[('환자',0.27),('회장',0.21),('거버넌스',0.19),('의원',0.18),('미국',0.18),('지방',0.17),('신규',0.15),('모빌리티',0.15),('라임',0.14),('자치',0.13)]