Sentiment Analysis Based on Micro Blog Hot Topics

Yan Gao; Wen Qing Zhao; Xiao Ke Hou

doi:10.4028/www.scientific.net/AMR.753-755.3056

Paper Titles

Design and Implementation of an Automatic Scoring Subjective Question System Based on Domain Ontology
p.3039

Intelligent Alarm Function Based on RIA Technology of Equipment Network Management Design and Implement
p.3043

Saliency Detection Algorithm Based on Watershed Method and Regional Spatial Attention Model
p.3047

Research on Image Sharpness Evaluation
p.3051

Sentiment Analysis Based on Micro Blog Hot Topics
p.3056

Tabu Search Algorithm for for Capacitated Vehicle Routing Problem
p.3060

An Action Classification Based on 2D2MSD
p.3064

DCS Simulation Training System Design and Implementation
p.3068

Research on the Construction of Personalized Active Information Service Model in Digital Library
p.3071

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 753-755Sentiment Analysis Based on Micro Blog Hot Topics

Sentiment Analysis Based on Micro Blog Hot Topics

Abstract:

Sentiment analysis on micro blog data has attracted much attention recently. In this paper, according to the characteristics of Chinese micro blog, a method based on semantic rules was provided to analysis emotional of micro blog hot topics. This method considers the adverbs of degree features, adverbs of negative features and emoticons features on the basis of emotional words in the micro blog text. Our proposed method is compared with the method based on emotional dictionary on experiment. The experimental results show that the proposed method is feasible and effective.

You might also be interested in these eBooks

Materials Processing and Manufacturing III

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 753-755)

Pages:

3056-3059

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.753-755.3056

Citation:

Cite this paper

Online since:

August 2013

Authors:

Yan Gao, Wen Qing Zhao, Xiao Ke Hou

Keywords:

Emotional Dictionary, Micro Blog, Semantic Rules, Sentiment Analysis

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Alec Go，Richa Bhayani，Lei Huang． Twitter Sentiment Classification using Distant Supervision[R]．Technical report，Stanford Digital Library Technologies Project. (2009).

Google Scholar

[2] Dmitry Davidiv，Oren Tsur，Ari Rappoport．Enhanced Sentiment Learning Using Twitter Hash—tagsand Smileys[C]/Coling 2010(poster paper)，pp．241—249. (2010).

Google Scholar

[3] Luciano Barbosa and Junlan Feng. 2010. Robust Sentiment Detection on Twitter from Biased and Noisy Data. In Coling 2010(poster paper), pp.36-44 . (2010).

Google Scholar

[4] Long Jiang, Mo Yu, Ming Zhou and Xiaohua Liu. 2011. Target-dependent Twitter Sentiment Classification. ACL 2011. (2011).

Google Scholar

[5] Xie Lixing. Sentiment analysis of Chinese microblog using SVM [D]. Tsinghua University，2011，4 (2011).

Google Scholar

[6] Liu Zhiming, Liu Lu. Empirical study of sentiment classification for Chinese microblog based on machine learning. Computer Engineering and Applications, Mag, 48(1), 1-4 (2012).

Google Scholar

[7] http: /www. keenage. com/html/c_index. html[EB/OL].

Google Scholar

[8] Lin Huang, Guo Shuhui. On the Characteristics, range and classification of adverbs of degree. Journal of Shanxi University ( Philosophy & Social Science)， Mag, 26( 2) , 71-74 (2003).

Google Scholar

[9] Hao Leihong. Research on negative aderbs of modern Chinese[D]. Beijing: Capital Normal University, (2003).

Google Scholar