Sentiment Classification Using Word Sub-sequences and Dependency Sub-trees

By Shotaro Matsumoto, Hiroya Takamura, Manabu Okumura. 2005

234 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Text Categorization as a Graph Classification Problem (2015, background): “The works of Jiang et al. (2010) and Arora et al. (2010) are also related but their representations are different and closer to parse and dependency trees used as base features for text categorization by Kudo and Matsumoto (2004) and Matsumoto et al. (2005).”
Dependency-based Convolutional Neural Networks for Sentence Embedding (2015, result): “…sentiment analysis literature, researchers have incorporated long-distance information from syntactic parse trees, but the results are somewhat inconsistent: some reported small improvements (Gamon, 2004; Matsumoto et al., 2005), while some otherwise (Dave et al., 2003; Kudo and Matsumoto, 2004).”
Classification of sentiment reviews using n-gram machine learning approach (2016, methodology): “, 2002) and (Matsumoto et al., 2005), have used unigram approach to classify the reviews.”
A survey on sentiment detection of reviews (2009, methodology): “In fact, most of the literatures showed that SVM and Naı̈ve Bayes are perfect methods in single domain document sentiment classification (Aue and Gamon, 2005; Beineke et al., 2004; Kennedy and Inkpen, 2006; Lin et al., 2006; Matsumoto et al., 2005; Mullen and Collier, 2004; Pang and Lee, 2004; Pang ”
Opinion Mining and Sentiment Analysis (2008, background): “More extensive comparisons of the performance of standard machine learning techniques with other types of features or feature selection schemes have been engaged in in later work [5, 69, 103, 204, 217]; see Section 4.”
Classifying sentiment in microblogs: is brevity an advantage? (2010, background): “The most promising results came from a POS-based stop-wording approach proposed by Matsumoto et al. ([9]).”
Sentiment Classification using Automatically Extracted Subgraph Features (2010, methodology): “We only use unigrams that occur in at least two sentences of the training data same as Matsumoto et al. (2005). We also filter out stop words using a small stop word list(9).”