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Unsupervised sentiment analysis with emotional signals
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In: http://www.public.asu.edu/~xiahu/papers/www13.pdf (2013)
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Informal Multilingual Multi-domain Sentiment Analysis
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In: http://www.informatica.si/PDF/37-4/13_Stajner - Informal multilingual multi-domain sentiment analysis.pdf (2013)
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Computational Linguistics (2012)" The French Social Media Bank: a Treebank of Noisy User Generated Content
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In: http://hal.inria.fr/docs/00/78/08/95/PDF/coling2012.pdf (2013)
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A Pointillism Approach for Natural Language Processing of Social Media
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In: http://www.cs.unm.edu/%7Ecrandall/64.pdf (2012)
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Dealing with input noise in statistical machine translation.” COLING
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In: http://aclweb.org/anthology/C/C12/C12-2032.pdf (2012)
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Mark my words! Linguistic style accommodation in social media
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In: http://www.cs.cornell.edu/~cristian/papers/accommodation.pdf (2011)
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Finding high-quality content in social media with an application to community-based question answering
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In: http://www.mathcs.emory.edu/~eugene/papers/wsdm2008quality.pdf (2008)
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Finding Romanized Arabic Dialect in Code-Mixed Tweets
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In: http://www.lrec-conf.org/proceedings/lrec2014/pdf/1116_Paper.pdf
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Approaches, Tools and Applications for Sentiment Analysis Implementation
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In: http://www.ijcaonline.org/research/volume125/number3/dandrea-2015-ijca-905866.pdf
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Two Database Resources for Processing Social Media English Text
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In: http://www.lrec-conf.org/proceedings/lrec2012/pdf/288_Paper.pdf
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Spanish Knowledge Base Generation for Polarity Classification from Masses ABSTRACT
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In: http://www2013.org/companion/p571.pdf
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Unsupervised sentiment analysis with emotional signals
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In: http://www2013.org/proceedings/p607.pdf
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Feel the Heat: Emotion Detection in Arabic Social Media Content
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In: http://sdiwc.net/digital-library/web-admin/upload-pdf/00001223.pdf
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The Call Triangle: student, teacher and institution Facebook used in a German film project
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In: http://files.eric.ed.gov/fulltext/ED542419.pdf
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Unsupervised sentiment analysis with emotional signals
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In: http://www.public.asu.edu/~xiahu/papers/www13.pdf
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Abstract:
The explosion of social media services presents a great opportunity to understand the sentiment of the public via analyzing its large-scale and opinion-rich data. In social media, it is easy to amass vast quantities of unlabeled data, but very costly to obtain sentiment labels, which makes unsupervised sentiment analysis essential for various applications. It is challenging for traditional lexicon-based unsupervised methods due to the fact that expressions in social media are unstructured, informal, and fast-evolving. Emoticons and product ratings are examples of emotional signals that are associated with sentiments expressed in posts or words. Inspired by the wide availability of emotional signals in social media, we propose to study the problem of unsupervised sentiment analysis with emotional signals. In particular, we investigate whether the signals can potentially help sentiment analysis by providing a unified way to model two main categories of emotional signals, i.e., emotion indication and emotion correlation. We further incorporate the signals into an unsupervised learning framework for sentiment analysis. In the experiment, we compare the proposed framework with the state-of-the-art methods on two Twitter datasets and empirically evaluate our proposed framework to gain a deep understanding of the effects of emotional signals.
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Keyword:
Emoticon; Experimentation Keywords Emotional Signals; General Terms Algorithm; Performance; Sentiment Analysis; Social Correlation; Social Media; Twitter
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URL: http://www.public.asu.edu/~xiahu/papers/www13.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.300.2960
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User Community Reconstruction using Sampled Microblogging Data
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In: http://www2012.wwwconference.org/proceedings/companion/p657.pdf
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A Content-Based Analysis of Travellers ’ Social Media
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In: http://world-comp.org/p2011/EEE8401.pdf
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Named Entity Trends Originating from Social Media
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In: http://www.aclweb.org/anthology/W/W12/W12-5501.pdf
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Mark My Words! Linguistic Style Accommodation in Social Media
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In: http://www.www2011india.com/proceeding/proceedings/p745.pdf
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Visualising Topical Sentiment in Twitter Streams
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In: http://oak.dcs.shef.ac.uk/smile2013/smile2013_paper2.pdf
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