SentiWordNet is a lexical resource for opinion mining. SentiWordNet assigns to each synset of WordNet three sentiment scores: positivity, negativity, objectivity
Web content mining is related but different from data mining and text mining. It is related to data mining because many data mining techniques can be applied in Web content mining. It is related to text mining because much of the web contents are texts. H
N. Archak, A. Ghose, and P. Ipeirotis. KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, page 56-65. New York, NY, USA, ACM, (2007)
Q. Mei, X. Ling, M. Wondra, H. Su, and C. Zhai. WWW '07: Proceedings of the 16th international conference on World Wide Web, page 171--180. New York, NY, USA, ACM, (2007)
T. Wilson, D. Pierce, and J. Wiebe. Proc. Meeting of Human Language Technologies-North American Chapter of the ACL (HLT-NAACL-2003) Companion Volume (software demonstration), (2005)
W. Lin, T. Wilson, J. Wiebe, and A. Hauptmann. Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL-2006), page 109--116. New York, New York, (2006)
B. Liu, M. Hu, and J. Cheng. WWW '05: Proceedings of the 14th international conference on World Wide Web, page 342--351. New York, NY, USA, ACM, (2005)
K. Dave, S. Lawrence, and D. Pennock. WWW '03: Proceedings of the 12th international conference on World Wide Web, page 519--528. New York, NY, USA, ACM, (2003)
C. Cardie, J. Wiebe, T. Wilson, and D. Litman. In Working Notes - New Directions in Question Answering (AAAI Spring Symposium Series, page 20--27. (2003)
N. Jindal, and B. Liu. WSDM '08: Proceedings of the international conference on Web search and web data mining, page 219--230. New York, NY, USA, ACM, (2008)