Contextual Valence Shifters

By Livia Polanyi, Annie Zaenen. 2006

622 citations recorded by the source corpus.

Subfields: Language and Linguistics, Natural Language Processing

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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).”
United we Stand: Improving Sentiment Analysis by Joining Machine Learning and Rule Based Methods (2010, background): “Valence shifters originally studied in (Polanyi and Zaenen, 2004) are contextual elements in discourse that could modify the valence of opinionated words, thus affecting the overall polarity of a sentence.”
SENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS (2006, methodology): “The term-counting method is easily modified to include the linguistic analysis of Polanyi and Zaenen (2004). We can directly measure the impact of valence shifters on sentiment classification.”
Articles: Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis (2009, background): “Polanyi and Zaenen (2004) give a detailed discussion of many of these types of polarity inﬂuencers.”
Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions (2011, background): “The work of (Polanyi and Zaenen, 2006; Choi and Cardie, 2008) focuses on manually constructing several lexica and rules for both polar words and related content-word negators, such as “prevent cancer”, where prevent reverses the negative polarity of cancer.”
Twitter Sentiment Analysis (2015, background): “Other researches introduce the concept of contextual valence shifter (Polanyi and Zaenen, 2006), which consists of negation, intensiﬁer and diminisher.”
Sentiment Analysis: An Overview from Linguistics (2016, neutral): “The effect of intensification has been accounted for using simple addition and subtraction (Kennedy & Inkpen 2006, Polanyi & Zaenen 2006).”
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].”
Survey on Aspect-Level Sentiment Analysis (2016, background): “A theoretical discussion by Polyani and Zaenen [85] proposes some foundational considerations when dealing with these contextual valence shifters as they are sometimes called.”
Multi-entity Sentiment Scoring (2009, neutral): “The model supports polarity-reversing compositions (cf. [ 14]) in which re-versive ( ¬ ) constituents reverse non-neutral ones (e.g. “[no [ ¬ ] talent ( + ) ] ( - ) ” ; “[tax ( - ) decreases [ ¬ ] ] ( + ) ” ), and the resolution of non-neutral polarity conﬂicts (e.g. “[bad ( - ) luck ( + ) ] ( - )…”
SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives (2016, background): “The only two words that have intrinsic polarity are shown in yellow color; the words that modify the meaning of other words in the manner similar to contextual valence shifters (Polanyi and Zaenen, 2006) are shown in blue.”
Neural Networks and Deep Learning (2018, neutral): “Such words are referred to as contextual valence shifters [377], and their effect can be modeled only in a sequence-centric setting.”