Abstract
Aspect-based sentiment analysis is an important tool to understand user opinions in a fine-grained manner. Although extensively studied, developing such a tool for a specific domain remains an expensive process. Most existing methods either rely on massive labeled data for training or external language resource and tools which are not necessarily available or accurate. We propose to study the aspect-based sentiment analysis with only a small set of aspect and sentiment seed words as guidance on a target corpus. We first expand the aspect and sentiment lexicons from the given seed words by features created by frequent pattern mining. Then, we develop a generative model to characterize the aspect and sentiment mentions based on their word embedding, and infer the sentiment polarity for sentiment words accordingly. The effectiveness of our method is verified by experiments on two real world data sets.
| Original language | English (US) |
|---|---|
| Title of host publication | SIAM International Conference on Data Mining, SDM 2019 |
| Publisher | Society for Industrial and Applied Mathematics Publications |
| Pages | 253-261 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781611975673 |
| State | Published - Jan 1 2019 |
| Event | 19th SIAM International Conference on Data Mining, SDM 2019 - Calgary, Canada Duration: May 2 2019 → May 4 2019 |
Publication series
| Name | SIAM International Conference on Data Mining, SDM 2019 |
|---|
Conference
| Conference | 19th SIAM International Conference on Data Mining, SDM 2019 |
|---|---|
| Country | Canada |
| City | Calgary |
| Period | 5/2/19 → 5/4/19 |
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ASJC Scopus subject areas
- Software
Cite this
Aspect-based sentiment analysis with minimal guidance. / Zhuang, Honglei; Hanratty, Timothy; Han, Jiawei.
SIAM International Conference on Data Mining, SDM 2019. Society for Industrial and Applied Mathematics Publications, 2019. p. 253-261 (SIAM International Conference on Data Mining, SDM 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - Aspect-based sentiment analysis with minimal guidance
AU - Zhuang, Honglei
AU - Hanratty, Timothy
AU - Han, Jiawei
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Aspect-based sentiment analysis is an important tool to understand user opinions in a fine-grained manner. Although extensively studied, developing such a tool for a specific domain remains an expensive process. Most existing methods either rely on massive labeled data for training or external language resource and tools which are not necessarily available or accurate. We propose to study the aspect-based sentiment analysis with only a small set of aspect and sentiment seed words as guidance on a target corpus. We first expand the aspect and sentiment lexicons from the given seed words by features created by frequent pattern mining. Then, we develop a generative model to characterize the aspect and sentiment mentions based on their word embedding, and infer the sentiment polarity for sentiment words accordingly. The effectiveness of our method is verified by experiments on two real world data sets.
AB - Aspect-based sentiment analysis is an important tool to understand user opinions in a fine-grained manner. Although extensively studied, developing such a tool for a specific domain remains an expensive process. Most existing methods either rely on massive labeled data for training or external language resource and tools which are not necessarily available or accurate. We propose to study the aspect-based sentiment analysis with only a small set of aspect and sentiment seed words as guidance on a target corpus. We first expand the aspect and sentiment lexicons from the given seed words by features created by frequent pattern mining. Then, we develop a generative model to characterize the aspect and sentiment mentions based on their word embedding, and infer the sentiment polarity for sentiment words accordingly. The effectiveness of our method is verified by experiments on two real world data sets.
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M3 - Conference contribution
AN - SCOPUS:85066106094
T3 - SIAM International Conference on Data Mining, SDM 2019
SP - 253
EP - 261
BT - SIAM International Conference on Data Mining, SDM 2019
PB - Society for Industrial and Applied Mathematics Publications
ER -