%0 Journal Article %1 noauthororeditor %A Malouf, Robert %A Mullen, Tony %D 2008 %J Internet Research %K discours political sentiment thema thema:userclassification user %P 177-190 %T Taking sides: user classification for informal online political discourse %U http://www.emeraldinsight.com/doi/pdfplus/10.1108/10662240810862239 %V 18 %X Purpose – To evaluate and extend, existing natural language processing techniques into the domain of informal online political discussions. Design/methodology/approach – A database of postings from a US political discussion site was collected, along with self‐reported political orientation data for the users. A variety of sentiment analysis, text classification, and social network analysis methods were applied to the postings and evaluated against the users' self‐descriptions. Findings – Purely text‐based methods performed poorly, but could be improved using techniques which took into account the users' position in the online community. Research limitations/implications – The techniques we applied here are fairly simple, and more sophisticated learning algorithms may yield better results for text‐based classification. Practical implications – This work suggests that social network analysis is an important tool for performing natural language processing tasks with informal web texts. Originality/value – This research extends sentiment analysis to a new subject domain (US politics) and a new text genre (informal online discusssions).

@article{noauthororeditor, abstract = {Purpose – To evaluate and extend, existing natural language processing techniques into the domain of informal online political discussions. Design/methodology/approach – A database of postings from a US political discussion site was collected, along with self‐reported political orientation data for the users. A variety of sentiment analysis, text classification, and social network analysis methods were applied to the postings and evaluated against the users' self‐descriptions. Findings – Purely text‐based methods performed poorly, but could be improved using techniques which took into account the users' position in the online community. Research limitations/implications – The techniques we applied here are fairly simple, and more sophisticated learning algorithms may yield better results for text‐based classification. Practical implications – This work suggests that social network analysis is an important tool for performing natural language processing tasks with informal web texts. Originality/value – This research extends sentiment analysis to a new subject domain (US politics) and a new text genre (informal online discusssions).}, added-at = {2017-04-19T21:10:24.000+0200}, author = {Malouf, Robert and Mullen, Tony}, biburl = {https://www.bibsonomy.org/bibtex/26ce2021d26a2342a1e2f72731bad60b3/lautenschlager}, description = {Taking sides: user classification for informal online political discourse: Internet Research: Vol 18, No 2}, interhash = {531bf2b7978fe71b83eb8dcd30da2af2}, intrahash = {6ce2021d26a2342a1e2f72731bad60b3}, journal = {Internet Research}, keywords = {discours political sentiment thema thema:userclassification user}, pages = {177-190}, timestamp = {2017-05-29T13:49:04.000+0200}, title = {Taking sides: user classification for informal online political discourse}, url = {http://www.emeraldinsight.com/doi/pdfplus/10.1108/10662240810862239}, volume = 18, year = 2008 }