This paper proposes neural networks for inte-grating compositional and non-compositional sentiment in the process of sentiment compo-sition, a type of semantic composition that op-timizes a sentiment objective. We enable in-dividual composition operations in a recursive process to possess

. In this work we present a novel approach to predict-ing economic variables using sentiment composition over text streams of Web data. We treat each text stream as a separate sentiment source with its own predictive distribu-tion. We then use a Bayesian classifier combination model to combine the separate

Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models

involving direct strength judgements on a 7-point scale and the other relying on a sentiment annotated corpus. The empirical evaluation of our model shows that it matches the way humans handle negation and modality in opinionated sentences. 1

What is sentiment analysis? The term has been around since 2000ish, and has been used to cover a variety of different phenomena: Sentiment proper Positive, negative, or neutral attitudes expressed in text: Suffice to say, Skyfall is one of the best Bonds in the 50-year history of moviedom’s most

Single-word vector space models have been very successful at learning lexical information. However, they cannot capture the compositional meaning of longer phrases, preventing them from a deeper understanding of language. We introduce a recursive neural network (RNN) model that learns compositional

In this paper we report research results investigating microblogging as a form of electronic word-of-mouth for sharing consumer opinions concerning brands. We analyzed more than 150,000 microblog postings containing branding comments, sentiments, and opinions.We investigated the overall structure

We present a general learning-based approach for phrase-level sentiment analysis that adopts an ordinal sentiment scale and is explicitly compositional in nature. Thus, we can model the compositional effects required for accurate assignment of phrase-level sentiment. For example, combining

in light of compositional semantics, and present a novel learningbased approach that incorporates structural inference motivated by compositional semantics into the learning procedure. Our experiments show that (1) simple heuristics based on compositional semantics can perform better than learning

We present a compositional framework for modelling entity-level sentiment (sub)contexts, and demonstrate how holistic multi-entity polarity scoring emerges as a by-product of compositional sentiment parsing. A data set of five annotators’ multi-entity judgements is presented, and a human ceiling