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This paper presents an opinion analysis system developed by CUHK_PolyU_Tsinghua Web Information Analysis Group (WIA), namely WIA-Opinmine, for NTCIR-7 MOAT Task. Different from most existing opinion mining systems, which recognize opinionated sentences as one-step classification procedure, WIA-Opinmine adopts a multi-pass coarse-fine analysis strategy. A(More)
In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main(More)
This paper presents the CUHK opinion analysis system, namely Opinmine, for the NTCIR-6 pilot task. Opinmine comprises of three functional modules: (1) Preprocessing and Assignment Module (PAM) performs word segmentation, part-of-speech (POS) tagging and named entity recognition on the input Chinese text. It is based on lexicalized Hidden Markov Model and(More)
Lyric-based song sentiment classification seeks to assign songs appropriate sentiment labels such as light-hearted and heavy-hearted. Four problems render vector space model (VSM)-based text classification approach ineffective: 1) Many words within song lyrics actually contribute little to sentiment; 2) Nouns and verbs used to express sentiment are(More)
The Web holds valuable, vast, and unstructured information about public opinion. Here, the history, current use, and future of opinion mining and sentiment analysis are discussed, along with relevant techniques and tools. of information were friends and specialized magazine or websites. Now, the " social web " provides new tools to efficiently create and(More)
This paper presents an opinion analysis system based on linguistic knowledge which is acquired from small-scale annotated text and raw topic-relevant webpage. Based on the observation on the annotated opinion corpus, some word-, collocation- and sentence-level linguistic features for opinion analysis are discovered. Supervised and unsupervised learning(More)