UGSD AAAI19 poster 3 - CSIEcjwang/data/AAAI2019_P.pdf · 2019. 2. 8. · UGSD: User Generated...
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UGSD: User Generated Sentiment Dictionaries from Online Customer Reviews Chun-Hsiang Wang, † Kang-Chun Fan, ‡ Chuan-Ju Wang, ‡ Ming-Feng Tsai † † National Chengchi University, Taiwan ‡ Academia Sinica, Taiwan CFDA-CLIP Labs Source Code: UGSD The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019) CFDA & CLIP Labs Motivation ✓ Constructing domain-specific sentiment dictionaries is important to sentiment analysis. ✓ Customer review platforms attain rich information about the ways that people convey their sentiment in certain domains. ✓ General sentiment dictionaries or annotated seed words greatly impact the results of dictionary construction. Contributions ✓ Data-driven dictionary: Requiring no additional annotation of seed words or external dictionaries. ✓ Domain-specific dictionaries: Applying to a variety of user-generated content from different domains. ✓ Application scalability: Producing representations of the learned sentiment words during the dictionary construction. Experiments Our framework: UGSD ✓ Leverage POS information ✓ Concatenate adverbs and adjectives ✓ Replace entities with ratings Review transformation ✓ Maximum-cosine-similarity scheme: ✓ Z-score scheme: Dictionary construction a ij = cos( ~ v s i , ~ v r j ) ( ) Representation learning ✓ Joint probability between words ✓ Minimize the distance between the empirical and learned distribution ✓ Replace the distance function with KL divergence p(i, j )= 1 1+ e - ~ v i | · ~ v j O = dist(ˆ p(·, ·),p(·, ·)) O = - X (i,j )2A f ij log p(i, j ) Amazon dictionaries
Transcript of UGSD AAAI19 poster 3 - CSIEcjwang/data/AAAI2019_P.pdf · 2019. 2. 8. · UGSD: User Generated...
UGSD:User Generated Sentiment Dictionariesfrom Online Customer ReviewsChun-Hsiang Wang,† Kang-Chun Fan,‡ Chuan-Ju Wang,‡ Ming-Feng Tsai†† National Chengchi University, Taiwan‡ Academia Sinica, Taiwan
CFDA-CLIP Labs
Source Code:UGSD
The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019) CFDA & CLIP Labs
Motivation
✓ Constructing domain-specific sentiment dictionaries is important to sentiment analysis.
✓ Customer review platforms attain rich information about the ways that people convey their sentiment in certain domains.
✓ General sentiment dictionaries or annotated seed words greatly impact the results of dictionary construction.
Contributions
✓ Data-driven dictionary: Requiring no additional annotation of seed words or external dictionaries.
✓ Domain-specific dictionaries: Applying to a variety of user-generated content from different domains.
✓ Application scalability: Producing representations of the learned sentiment words during the dictionary construction.
Experiments
Our framework: UGSD
✓ Leverage POS information
✓ Concatenate adverbs and adjectives
✓ Replace entities with ratings
Review transformation
✓ Maximum-cosine-similarity scheme:
✓ Z-score scheme:
Dictionary construction
aij = cos(~vsi ,~vrj )
( )
Representation learning
✓ Joint probability between words
✓ Minimize the distance between the empirical and learned distribution
✓ Replace the distance function with KL divergence
p(i, j) =1
1 + e�~vi|·~vj
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O = �X
(i,j)2A
fij log p(i, j)
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Amazon dictionaries
