Distributional Semantic Models for Affective Text Analysis

Abstract

We present an affective text analysis model that can directly estimate and combine affective ratings of multi-word terms, with application to the problem of sentence polarity/semantic orientation detection. Starting from a hierarchical compositional method for generating sentence ratings, we expand the model by adding multi-word terms that can capture non-compositional semantics. The method operates similarly to a bigram language model, using bigram terms or backing off to unigrams based on a (degree of) compositionality criterion. The affective ratings for n-gram terms of different orders are estimated via a corpus-based method using distributional semantic similarity metrics between unseen words and a set of seed words. N-gram ratings are then combined into sentence ratings via simple algebraic formulas. The proposed framework produces state-of-the-art results for word-level tasks in English and German and the sentence-level news headlines classification SemEval'07-Task14 task. The inclusion of bigram terms to the model provides significant performance improvement, even if no term selection is applied.