TechTalks from event: NAACL 2015
9A: Lexical Semantics and Sentiment Analysis
A Corpus and Model Integrating Multiword Expressions and SupersensesThis paper introduces a task of identifying and semantically classifying lexical expressions in context. We investigate the online reviews genre, adding semantic supersense annotations to a 55,000 word English corpus that was previously annotated for multiword expressions. The noun and verb supersenses apply to full lexical expressions, whether single- or multiword. We then present a sequence tagging model that jointly infers lexical expressions and their supersenses. Results show that even with our relatively small training corpus in a noisy domain, the joint task can be performed to attain 70% class labeling F1.
Good News or Bad News: Using Affect Control Theory to Analyze Readers' Reaction Towards News ArticlesThis paper proposes a novel approach to sentiment analysis that leverages work in sociology on symbolic interactionism. The proposed approach uses Affect Control Theory (ACT) to analyze readers' sentiment towards factual (objective) content and towards its entities (subject and object). ACT is a theory of affective reasoning that uses empirically derived equations to predict the sentiments and emotions that arise from events. This theory relies on several large lexicons of words with affective ratings in a three-dimensional space of evaluation, potency, and activity (EPA). The equations and lexicons of ACT were evaluated on a newly collected news-headlines corpus. ACT lexicon was expanded using a label propagation algorithm, resulting in 86,604 new words. The predicted emotions for each news headline was then computed using the augmented lexicon and ACT equations. The results had a precision of 82%, 79%, and 68% towards the event, the subject, and object, respectively. These results are significantly higher than those of standard sentiment analysis techniques.
Do We Really Need Lexical Information? Towards a Top-down Approach to Sentiment Analysis of Product ReviewsMost of the current approaches to sentiment analysis of product reviews are dependent on lexical sentiment information and proceed in a bottom-up way, adding new layers of features to lexical data. In this paper, we maintain that a typical product review is not a bag of sentiments, but a narrative with an underlying structure and reoccurring patterns, which allows us to predict its sentiments knowing only its general polarity and discourse cues that occur in it. We hypothesize that knowing only the reviews score and its discourse patterns would allow us to accurately predict the sentiments of its individual sentences. The experiments we conducted prove this hypothesis and show a substantial improvement over the lexical baseline.