a r t i c l e i n f o Based on the underestimation model in bear markets and the overestimation model in bull markets, we propose two types of sentiment asset pricing models to study the effects of investor sentiment on stock prices and limit of arbitrage. The two sentiment asset pricing models demonstrate that investor sentiment has a systematic and significant impact on stock prices; furtherm...

Financial market prediction on the basis of online sentiment tracking has drawn a lot of attention recently. However, most results in this emerging domain rely on a unique, particular combination of data sets and sentiment tracking tools. This makes it difficult to disambiguate measurement and instrument effects from factors that are actually involved in the apparent relation between online sen...

In this paper, we propose to build large-scale sentiment lexicon from Twitter with a representation learning approach. We cast sentiment lexicon learning as a phrase-level sentiment classification task. The challenges are developing effective feature representation of phrases and obtaining training data with minor manual annotations for building the sentiment classifier. Specifically, we develo...

Facet-based sentiment analysis involves discovering the latent facets, sentiments and their associations. Traditional facet-based sentiment analysis algorithms typically perform the various tasks in sequence, and fail to take advantage of the mutual reinforcement of the tasks. Additionally, inferring sentiment levels typically requires domain knowledge or human intervention. In this paper, we p...

Sentiment Analysis, an important area of Natural Language Understanding, often relies on the assumption that lexemes carry inherent sentiment values, as reflected in specialized resources. We examine and measure the contribution that eight intensifying adverbs make to the sentiment value of sentences, as judged by human annotators. Our results show, first, that the intensifying adverbs are not ...

Sentiment analysis has undergone a shift from document-level analysis, where labels expresses the sentiment of a whole document or whole sentence, to subsentential approaches, which assess the contribution of individual phrases, in particular including the composition of sentiment terms and phrases such as negators and intensifiers. Starting from a small sentiment treebank modeled after the Sta...

This paper presents a rich annotation scheme for mentions, co-reference, meronymy, sentiment expressions, modifiers of sentiment expressions including neutralizers, negators, and intensifiers, and describes a large corpus annotated with this scheme. We describe how this corpus relates to recent, state-of-the-art work in sentiment analysis, and define the various annotation types, provide exampl...

The sentiment detection of texts has been witnessed a booming interest in recent years, due to the increased availability of online reviews in digital form and the ensuing need to organize them. Till to now, there are mainly four different problems predominating in this research community, namely, subjectivity classification, word sentiment classification, document sentiment classification and ...

We present two NLP components for the Story Cloze Task – dictionary-based sentiment analysis and lexical cohesion. While previous research found no contribution from sentiment analysis to the accuracy on this task, we demonstrate that sentiment is an important aspect. We describe a new approach, using a rule that estimates sentiment congruence in a story. Our sentiment-based system achieves str...

In this paper, we present a novel method that integrates domain sentiment knowledge into the analysis approach to deal with feature-level opinion mining By constructing a domain ontology called Fuzzy Domain Sentiment Ontology Tree (FDSOT), we then utilize the prior sentiment knowledge of our ontology to achieve significantly accuracy in sentiment classification. Particularly, the FDSOT is the c...