Sentiment Analysis and Opinion Mining Keynote Address

被引:0
作者
Gelbukh, Alexander [1 ]
机构
[1] Inst Politecn Nacl, CIC, Mexico City, DF, Mexico
来源
2017 6TH INTERNATIONAL CONFERENCE ON RELIABILITY, INFOCOM TECHNOLOGIES AND OPTIMIZATION (TRENDS AND FUTURE DIRECTIONS) (ICRITO) | 2017年
关键词
sentiment analysis; sentic computing; opinion mining; deep learning; machine-learning; BASIC EMOTIONS; PATTERNS; SENTICNET;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Sentiment analysis and opinion mining are closely related tasks that recently have received great attention from both research community and industry. Opinion mining is aimed to turn a huge amount of user-contributed context in Internet, such as tweets, product reviews, and blogs into useful information, which allows the companies and the governmental bodies to improve their product and services, and, on the other hand, allows the consumers to make informed buying decisions. Sentiment analysis, apart from its use in opinion mining, finds other important applications in security, healthcare, and education, among others. In this paper, I briefly discuss the motivation behind these tasks and outline some techniques recently developed by our group, mainly based on specially tailored deep-learning architectures or other machine-learning methods.
引用
收藏
页码:41 / 47
页数:7
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