|Table of Contents|

A Matching Algorithm Between the Tweets in Twitter and SentiWordNet(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2016年03期
Page:
41-
Research Field:
计算机工程
Publishing date:

Info

Title:
A Matching Algorithm Between the Tweets in Twitter and SentiWordNet
Author(s):
Yi Shunming1; Zhou Hongbin1; Zhou Guodong2
（1.Department of Electronics and Information Engineering，Shazhou Professional Institute of Technology，Suzhou 215600，China）（2.School of Computer Science and Technology，Soochow University，Suzhou 215006，China）
Keywords:
tweets; sentiment classification; SentiWordNet; matching algorithm
PACS:
TP391
DOI:
10.3969/j.issn.1672-1292.2016.03.007
Abstract:
In the research of the Twitter sentiment classification，a method is widely used to obtain sentiment values by mapping tweets’ words with the synonym terms in the sentiment lexicon. However，tweets are usually written informally，which contain slangs，abbreviations and special symbols，many words in the tweets cannot be found in the terms of sentiment lexicon. Lower matching rate directly impacts the performance of sentiment classification. Based on the features of Twitter，a set of matching algorithm between tweets and sentiment lexicon SentiWordNet is proposed in the article. In this method，tweets are processed by data cleaning，alternative processing，POS tagging and word lemmatizing，along with some algorithms such as named entity recognition，hashtags word segmentation，negated context recognition with Word Clusters and phrase matching. Experimental results show that the matching rate reaches over 90%.

References:

［1］ HU M Q，LIU B. Mining and summarizing customer reviews［C］//Proceedings of KDD，USA，2004：168-177.
［2］ WIEBE J，BRUCE R，O’HARA T. Development and use of a gold standard dataset for subjectivity classifications［C］//Proceedings of ACL，USA，1999：246-253.
［3］ DAVE K，LAWRENCE S，PENNOCK D. Mining the peanut gallery：opinion extraction and semantic classification of product reviews［C］//Proceedings of WWW，Hungary，2003：519-528.
［4］ TURNEY P. Thumps up or thumbs down？semantic orientation applied to unsupervised classification of reviews［C］//Proceedings of ACL，USA，2002：417-424.
［5］ YU H，HATZIVASSILOGLOU V. Towards answering opinion questions：separating facts from opinions and identifying the polarity of opinion sentences［C］//Proceedings of EMNLP，Japan，2003：129-136.
［6］ ESULI A，SEBASTIANI F. Determining term subjectivity and term orientation for opinion mining［C］//Proceedings of EACL，Italy，2006：193-200.
［7］ PANG B，LEE L，VAITHYANATHAN S. Thumbs up？sentiment classification using machine learning techniques［C］//Proceedings of the EMNLP，USA，2002：79-86.
［8］ MEI Q，LING X，WONDRA M，et al. Topic sentiment mixture modeling facets and opinions in weblogs［C］//Proceedings of WWW，Canada，2007：171-180.
［9］ LI S，HUANG C，ZHOU G，et al. Employing personal/impersonal views in supervised and semi-supervised sentiment classification［C］//Proceedings of ACL，Sweden，2010：414-423.
［10］ LI S，WANG Z，ZHOU G，et al. Semi-supervised learning for imbalanced sentiment classification［C］//Proceedings of IJCAI，Spain，2011：1 826-1 831.
［11］ LI S，HUANG L，WANG R，et al. Sentence-level emotion classification with label and context dependence［C］//Proceedings of ACL，China，2015：1 045-1 053.
［12］ SOCHER R，PERELYGIN A，WU J，et al. Recursive deep models for semantic compositionality over a sentiment treebank［C］//Proceedings of EMNLP，USA，2013：1 631-1 642.
［13］ ZHU T，ZHANG F，LAN M. ECNUCS：A surface information based system description of sentiment analysis in Twitter in the SemEval-2013（Task 2）［C］//Proceedings of SemEval2013，USA，2013：408-413.
［14］ TANG D，WEI F，QIN B，et al. Coooolll：a deep learning system for Twitter sentiment classification［C］//Proceedings of SemEval2014，Ireland，2014：208-212.
［15］ 易顺明，易昊，周国栋. 基于情感特征向量的Twitter情感分类方法研究［C］//第14届全国计算语言学会议，广州，2015：79.
YI S M，YI H，ZHOU G D. Twitter sentiment classification with sentimental feature vector［C］//Proceedings of CCL2015，Guangzhou，2015：79. （in Chinese）
［16］ KALCHBRENNER N，GREFENSTETTE E，BLUNSOM P. A convolutional neural network for modeling sentences［C］//Proceedings of ACL，USA，2014：655-665.
［17］ BACCIANELLA S，ESULI A，SEBASTIANI F. SENTIWORDNET 3.0：An enhanced lexical resource for sentiment analysis and opinion mining［C］//Proceedings of LREC，Malta，2010：83-90.
［18］ OWOPUTI O，O’CONNOR B，DYER C，et al. Improved part-of-speech tagging for online conversational text with word clusters［C］//Proceedings of NAACL，USA，2013：380-390.
［19］ BROWN P，DESOUZA P，MERCER R，et al. Classbased n-gram models of natural language［J］. Computational linguistics，1997，18（4）：467-479.
［20］ NAKOV P，KOZAREVA Z，RITTER A，et al. SemEval-2013 task 2：sentiment analysis in Twitter［C］//Proceedings of SemEval2013，USA，2013：312-320.
［21］ POURSEPANJ H，WEISSBOCK J，INKPEN D. uOttawa：System description for SemEval 2013 task 2 sentiment analysis in Twitter［C］//Proceedings of SemEval2013，USA，2013：380-383.

Memo

Memo:
-
Last Update: 2016-09-30