| 1 |
Unsupervised sentiment analysis with emotional signals
|
|
|
|
In: http://www.public.asu.edu/~xiahu/papers/www13.pdf (2013)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Informal Multilingual Multi-domain Sentiment Analysis
|
|
|
|
In: http://www.informatica.si/PDF/37-4/13_Stajner - Informal multilingual multi-domain sentiment analysis.pdf (2013)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Computational Linguistics (2012)" The French Social Media Bank: a Treebank of Noisy User Generated Content
|
|
|
|
In: http://hal.inria.fr/docs/00/78/08/95/PDF/coling2012.pdf (2013)
|
|
BASE
|
|
Show details
|
|
|
| 4 |
A Pointillism Approach for Natural Language Processing of Social Media
|
|
|
|
In: http://www.cs.unm.edu/%7Ecrandall/64.pdf (2012)
|
|
Abstract:
The Chinese language poses challenges for natural language processing based on the unit of a word even for formal uses of the Chinese language, social media only makes word segmentation in Chinese even more difficult. In this document we propose a pointillism approach to natural language processing. Rather than words that have individual meanings, the basic unit of a pointillism approach is trigrams of characters. These grams take on meaning in aggregate when they appear together in a way that is correlated over time. Our results from three kinds of experiments show that when words and topics do have a meme-like trend, they can be reconstructed from only trigrams. For example, for 4-character idioms that appear at least 99 times in one day in our data, the unconstrained precision (that is, precision that allows for deviation from a lexicon when the result is just as correct as the lexicon version of the word or phrase) is 0.93. For longer words and phrases collected from Wiktionary, including neologisms, the unconstrained precision is 0.87. We consider these results to be very promising, because they suggest that it is feasible for a machine to reconstruct complex idioms, phrases, and neologisms with good precision without any notion of words. Thus the colorful and baroque uses of language that typify social media in challenging languages such as Chinese may in fact be accessible to machines.
|
|
Keyword:
A. Shu; Neologisms; Social Media; Trend Analysis. 1 2 P. Song; Trigram
|
|
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.259.7725
http://www.cs.unm.edu/%7Ecrandall/64.pdf
|
|
BASE
|
|
Hide details
|
|
|
| 5 |
Dealing with input noise in statistical machine translation.” COLING
|
|
|
|
In: http://aclweb.org/anthology/C/C12/C12-2032.pdf (2012)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Informal language learning setting: Technology or social interaction? The Turkish Online
|
|
|
|
In: http://www.tojet.net/articles/v11i2/11215.pdf (2012)
|
|
BASE
|
|
Show details
|
|
|
| 7 |
Mark my words! Linguistic style accommodation in social media
|
|
|
|
In: http://www.cs.cornell.edu/~cristian/papers/accommodation.pdf (2011)
|
|
BASE
|
|
Show details
|
|
|
| 8 |
Finding high-quality content in social media with an application to community-based question answering
|
|
|
|
In: http://www.mathcs.emory.edu/~eugene/papers/wsdm2008quality.pdf (2008)
|
|
BASE
|
|
Show details
|
|
|
| 9 |
Privacy in Video Media Spaces
|
|
|
|
In: http://grouplab.cpsc.ucalgary.ca/grouplab/uploads/Publications/Publications/2003-LexiconPrivacy.Report2003-724-27.pdf (2005)
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Finding Romanized Arabic Dialect in Code-Mixed Tweets
|
|
In: http://www.lrec-conf.org/proceedings/lrec2014/pdf/1116_Paper.pdf
|
|
BASE
|
|
Show details
|
|
|
| 11 |
Approaches, Tools and Applications for Sentiment Analysis Implementation
|
|
|
|
In: http://www.ijcaonline.org/research/volume125/number3/dandrea-2015-ijca-905866.pdf
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Two Database Resources for Processing Social Media English Text
|
|
|
|
In: http://www.lrec-conf.org/proceedings/lrec2012/pdf/288_Paper.pdf
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Spanish Knowledge Base Generation for Polarity Classification from Masses ABSTRACT
|
|
|
|
In: http://www2013.org/companion/p571.pdf
|
|
BASE
|
|
Show details
|
|
|
| 14 |
Unsupervised sentiment analysis with emotional signals
|
|
|
|
In: http://www2013.org/proceedings/p607.pdf
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Feel the Heat: Emotion Detection in Arabic Social Media Content
|
|
|
|
In: http://sdiwc.net/digital-library/web-admin/upload-pdf/00001223.pdf
|
|
BASE
|
|
Show details
|
|
|
| 16 |
The Call Triangle: student, teacher and institution Facebook used in a German film project
|
|
|
|
In: http://files.eric.ed.gov/fulltext/ED542419.pdf
|
|
BASE
|
|
Show details
|
|
|
| 17 |
Unsupervised sentiment analysis with emotional signals
|
|
|
|
In: http://www.public.asu.edu/~xiahu/papers/www13.pdf
|
|
BASE
|
|
Show details
|
|
|
| 18 |
User Community Reconstruction using Sampled Microblogging Data
|
|
|
|
In: http://www2012.wwwconference.org/proceedings/companion/p657.pdf
|
|
BASE
|
|
Show details
|
|
|
| 19 |
A Content-Based Analysis of Travellers ’ Social Media
|
|
|
|
In: http://world-comp.org/p2011/EEE8401.pdf
|
|
BASE
|
|
Show details
|
|
|
| 20 |
Movement in Learning: Revitalizing the Classroom
|
|
|
|
In: http://digitalcollections.sit.edu/cgi/viewcontent.cgi?article%3D1544%26context%3Dipp_collection
|
|
BASE
|
|
Show details
|
|
|
|
|
