Sentiment Composition

By Karo Moilanen, Stephen G. Pulman, Karo, Moilanen. 2007

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

Citations in the corpus

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank (2013, background): “Several works have explored sentiment compositionality through careful engineering of features or polarity shifting rules on syntactic structures (Polanyi and Zaenen, 2006; Moilanen and Pulman, 2007; Rentoumi et al., 2010; Nakagawa et al., 2010).”
Good debt or bad debt: Detecting semantic orientations in economic texts (2013, positive): “…beneﬁted from the recent advances in statistical nat-ural language processing and machine learning (Thelwall, Buckley, Paltoglou, & Kappas, 2010; Thelwall, Buckley, & Paltoglou, 2012; Moilanen et al., 2010; Moilanen & Pulman, 2007; Pang, Lee, & Vaithyanathan, 2002; Wilson, Wiebe, & Ho ﬀ man, 2009).”
Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing (2019, background): “, 2010) to sentiment classification (Moilanen and Pulman, 2007; Socher et al., 2013), underlying model biases end up affecting the per-”
Dependency Tree-based Sentiment Classification using CRFs with Hidden Variables (2010, methodology): “Syntactic structures were used in the studies of Moilanen and Pulman (2007) and, Jia et al. (2009), but their methods are based on rules and supervised learning was not used to handle polarity reversal.”
Long Short-Term Memory Over Recursive Structures (2015, methodology): “Following (Socher et al., 2013), we also use the classification accuracy to measure the performances.”
Baselines and Bigrams: Simple, Good Sentiment and Topic Classification (2012, methodology): “Some previous work on classifying snippets include using pre-defined polarity reversing rules (Moilanen and Pulman, 2007), and learning complex models on parse trees such as in (Nakagawa et al.”
United we Stand: Improving Sentiment Analysis by Joining Machine Learning and Rule Based Methods (2010, background): “Moilanen and Pulman (Moilanen and Pulman, 2007) as well adopt a compositional approach to sentiment analysis.”
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research (2020, methodology): “This led to the emergence of research on valence shifters that incorporated changes in valence and polarity of terms based on contextual usage, Polanyi & Zaenen [170], Moilanen & Pulman [151].”
Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis (2008, methodology): “( Liang et al., 2008) Moilanen and Pulman (2007), on the other hand, handle the structural nature of the interactions more directly using the ideas from compositional semantics (e.g., Montague (1974), Dowty et al. (1981)).”
Twitter Sentiment Analysis (2015, unknown): “1 lexicon (Moilanen and Pulman, 2007).”
Compositional Matrix-Space Models for Sentiment Analysis (2011, background): “One notable exception is Moilanen and Pulman (2007), who propose a compositional semantic approach to assign a positive or negative sentiment to newspaper article titles.”
Lexicon-Based Methods for Sentiment Analysis (2011, background): “We also discuss, in Section 2.4, work on incorporating linguistic insights for the treatment of negation (Moilanen and Pulman 2007; Choi and Cardie 2008).”
Multi-entity Sentiment Scoring (2009, methodology): “One of the most recent developments in the area - compositional semantics - has shown potential for sentence-and expression-level analysis in both logic-oriented [11],[9] and machine learning-oriented [3] paradigms.”
Grammatical structures for word-level sentiment detection (2012, background): “In addition to being theoretically sound — a lacuna identiﬁed in many sentiment systems — such approaches improve downstream sentiment tasks (Moilanen and Pulman, 2007).”
Survey on Aspect-Level Sentiment Analysis (2016, background): “described in [86], where many of the ideas of Polyani and Zaenen are encoded in rules.”