Measuring praise and criticism

Abstract

________________________________________________________________________The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and negative semantic orientation indicates criticism (e.g., "disturbing", "superfluous"). Semantic orientation varies in both direction (positive or negative) and degree (mild to strong). An automated system for measuring semantic orientation would have application in text classification, text filtering, tracking opinions in online discussions, analysis of survey responses, and automated chat systems (chatbots). This paper introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words. Two instances of this approach are evaluated, based on two different statistical measures of word association: pointwise mutual information (PMI) and latent semantic analysis (LSA). The method is experimentally tested with 3,596 words (including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive (1,614 words) and negative (1,982 words). The method attains an accuracy of 82.8% on the full test set, but the accuracy rises above 95% when the algorithm is allowed to abstain from classifying mild words.

Estimating affective word covariates using word association data

“…The first extrapolation method we employed, Orientation towards Paradigm Words, predicted a word's valence, arousal, and dominance using that word's similarity towards certain paradigm words, words commonly used to describe extreme values on these dimensions (Kamps, Marx, Mokken, & de Rijke, 2004;Turney & Littman, 2003). Paradigm words were obtained from the instructions in the rating task described by Moors et al (2013), which yielded two positive and two negative paradigm words for each dimension (Table 1).…”

“…Once pairwise similarity estimates have been derived by applying either LSA or PMI to text corpora, one can estimate words' values on various dimensions using their similarity towards words for which the values on those dimensions are already known. Turney and Littman (2003) predicted the valence of words using their similarity to a small number of paradigm words, words commonly used to describe very low or very high levels of valence (e.g., good, bad). They compared the predictions of this approach with binary manual ratings (words rated positive or negative) for 3,596 English words, and report a correlation of .65 when using similarity derived from LSA (on a corpus comprising 10 million tokens), and between .61 (corpus containing 10 million tokens) and .83 (corpus containing 100 billion tokens) when using similarity derived from PMI.…”

Measuring news bias: Russia’s official news agency ITAR-TASS’ coverage of the Ukraine crisis

“…Both geographical classification and framing analysis were accomplished by employing computerized content analysis, which relies on dictionaries constructed by lexicon expansion techniques (c.f. Pang & Lee, 2008;Turney & Littman, 2003). The geographical dictionary comprises not only names of places but also of institutions and persons related to the crisis for a higher classification accuracy.…”

Sentiment lexicon adaptation with context and semantics for the social web

“…Several approaches have been built and used for extracting the words' contextual sentiment following the above principle [37,18]. In this paper, we use the SentiCircle approach [27], which similarly to other frequency-based approaches, it detects the context of a term from its co-occurrence patterns with other terms in tweets.…”

“…In this paper we compare our adaptation models against the semantic orientation by association approach (SO) [37], due to its effectiveness and simple implementation. To generate a sentiment lexicon, this approach starts with a balanced set of 14 positive and negative paradigm words (e.g., good, nice, nasty, poor).…”