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http://dx.doi.org/10.3745/KTSDE.2016.5.8.377

Developing a Sentiment Analysing and Tagging System  

Lee, Hyun Gyu (한국교통대학교 컴퓨터정보공학과)
Lee, Songwook (한국교통대학교 컴퓨터정보공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.5, no.8, 2016 , pp. 377-384 More about this Journal
Abstract
Our goal is to build the system which collects tweets from Twitter, analyzes the sentiment of each tweet, and helps users build a sentiment tagged corpus semi-automatically. After collecting tweets with the Twitter API, we analyzes the sentiments of them with a sentiment dictionary. With the proposed system, users can verify the results of the system and can insert new sentimental words or dependency relations where sentiment information exist. Sentiment information is tagged with the JSON structure which is useful for building or accessing the corpus. With a test set, the system shows about 76% on the accuracy in analysing the sentiments of sentences as positive, neutral, or negative.
Keywords
Sentiment Analysis; Twitter; Sentiment Tagged Corpus;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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1 P. D. Turney, "Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews," Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL '02), pp.417-424, 2002.
2 Johan Bollen, Huina Mao, and Xiao-Jun Zeng, "Twitter mood predicts the stock market," Journal of Computational Science, Vol.2, No.1, pp.1-8, 2011.   DOI
3 Alexander Pak and Patrick Paroubek, "Twitter as a Corpus for Sentiment Analysis and Opinion Mining," Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC '10), pp.1320-1326, 2010.
4 Alec Go, Richa Bhayani, and Lei Huang, "Twitter Sentiment Classification using Distant Supervision," Technical Report CS224N, Stanford University, 2009.
5 Songwook Lee, "Sentiment Analysis System Using Stanford Sentiment Treebank," Journal of the Korean Society of Marine Engineering, Vol.39, No.3, pp.274-279, 2015.   DOI
6 Kong Joo Lee and Songwook Lee, "Error-driven Noun-Connection Rule Extraction for Morphological Analysis," Journal of the Korean Society of Marine Engineering, Vol.36, No.8, pp.1123-1128, 2012.   DOI
7 Gyoung-Ho Lee and Kong Joo Lee, "Design of a Reputation System for Twitter," Proceedings of the 24th Annual Conference on Human and Cognitive Language Technology, pp.62-66, 2012.
8 Johan Bollen, Huina Mao, and Xiao-Jun Zeng, "Twitter mood predicts the stock market," Journal of Computational Science, Vol.2, No.1, pp.1-8, 2011.   DOI
9 Sitaram Asur and Bernardo A. Huberman, "Predicting the Future With Social Media," Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Vol.1, pp.492-499, 2010.
10 Gyoung-Ho Lee and Kong Joo Lee, "Twitter Sentiment Analysis for the Recent Trend Extracted from the Newspaper Article," The KIPS Transactions Part B, Vol.2B No.10, pp.731-738, 2013
11 Bing Liu, Minqing Hu, and Junsheng Cheng, "Opinion Observer: Analyzing and Comparing Opinions on the Web," Proceedings of the 14th International World Wide Web Conference, pp.342-451, 2005.
12 Richard Socher, Alex Perelygin, Jean Y Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts Potts, "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank," Proceedings of Conference on Empirical Methods on Natural Language Processing, 2013.
13 R. Feldman, "Techniques and applications for sentiment analysis," Communications of the ACM, Vol.56, No.4, pp.82-89, 2013.   DOI
14 Kong Joo Lee, "Compositional rules of Korean auxiliary predicates for sentiment analysis," Journal of the Korean Society of Marine Engineering, Vol.37, No.3, pp.291-299, 2013.   DOI
15 Ana-Maria Popescu and Oren Etzioni, "Extracting Product Features and Opinions from Reviews," Proceedings of Conference on Empirical Methods on Natural Language Processing, pp.339-346, 2005.
16 Brendan O'Connor, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A. Smith, "From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series," Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, pp.122-129, 2010.
17 Andrea Esuli and Fabrizio Sebastiani, "SENTIWORDNET: A publicly available lexical resource for opinion mining," in Proceedings of the 5th Conference on Language Resources and Evaluation (LREC '06), pp.417-422, 2006.
18 Woo Chul Lee, Hyun Ah Lee, and Kong Joo Lee, "Product Evaluation Summarization Through Linguistic Analysis of Product Reviews," The KIPS Transactions Part B, Vol.17B No.1, pp.93-98, 2010.   DOI