Opinion Extraction, Summarization and Tracking in News and Blog Corpora
By Lun-Wei Ku, Yu-Ting Liang, Hsin-Hsi Chen. AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, 2006
551 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/d78b8c05e8d3eacc7c58865cff09dd29d3815ae8
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Citations in the corpus
- Survey on Aspect-Level Sentiment Analysis (2016, methodology): “When the timestamp of each review is available, a timeline [92] could also be generated to show the change in sentiment over time.”
- Opinion Mining and Sentiment Analysis (2008, background): “Clearly, one can create a sentiment timeline simply by plotting the value of a chosen summary statistic at different times; the chosen statistic can reflect the prevailing polarity [170, 297] or simply the number of mentions, in which case what is being measured is perhaps not so much public opinion”
- Bilingual Co-Training for Sentiment Classification of Chinese Product Reviews (2011, background): “In recent years, sentiment classification has drawn much attention in the natural language processing (NLP) field and it has many useful applications, such as opinion mining and summarization (Liu, Hu, and Cheng 2005; Ku, Liang, and Chen 2006; Titov and McDonald 2008).”
- Survey on mining subjective data on the web (2011, methodology): “It is also interesting to mention, that applications working with the Chinese language are able to recognize polarity even for unseen words, thanks to the fact that phonetic characters determine the word's sense (Ku et al. 2006, 2007).”
- Challenges in Sentiment Analysis (2017, background): “However, there is interesting work in sentiment analysis of documents to generate text summaries (Ku, Liang, & Chen, 2006; Liu, Cao, Lin, Huang, & Zhou, 2007; Somprasertsri & Lalitrojwong, 2010; Stoyanov & Cardie, 2006; Lloret, Balahur, Palomar, & Montoyo, ), as well as detecting the patterns of…”
- Sentiment Analysis and Subjectivity (2010, background): “, producing a short textual summary based on multiple reviews or even a single review [4, 9, 52, 83, 88].”
- Aspect Extraction through Semi-Supervised Modeling (2012, background): “One approach is to find frequent noun terms and possibly with the help of dependency relations (Hu and Liu, 2004; Popescu and Etzioni, 2005; Zhuang et al., 2006; Blair-Goldensohn et al., 2008; Ku et al., 2006; Wu et al., 2009; Somasundaran and Wiebe, 2009; Qiu et al., 2011).”
- Sentiment Analysis and Opinion Mining (2012, methodology): “A series of joint models were also proposed in Lakkaraju et al. (2011) based on the composite topic model of HMM-LDA in Griffiths et al.”
- A Survey of Opinion Mining and Sentiment Analysis (2012, unknown): “, producing a short text summary [4, 11, 51, 89, 91].”
- Radical-Based Hierarchical Embeddings for Chinese Sentiment Analysis at Sentence Level (2017, methodology): “Like we did before, we collect all the sentiment words from two different popular Chinese sentiment lexicons, Hownet (Dong and Dong 2006) and NTUSD (Ku, Liang, and Chen 2006) and break them into radicals.”
- Identifying comparative sentences in text documents (2006, background): “Other related works on sentiment classification and opinions discovery include [9, 15, 16, 23, 27, 33, 34, 35].”
- A Review of Sentiment Analysis Research in Chinese Language (2017, unknown): “Xu et al. [15] have presented a method that utilizes a large unlabeled corpus to correct and expand two popular Chinese sentiment dictionaries, such as HowNet and NTUSD [47].”
- Co-Training for Cross-Lingual Sentiment Classification (2009, background): “In recent years, sentiment classification has drawn much attention in the NLP field and it has many useful applications, such as opinion mining and summarization (Liu et al., 2005; Ku et al., 2006; Titov and McDonald, 2008).”