We present Sentiment Analyzer (SA) that extracts sentiment (or opinion) about a subject from online text documents. Instead of classifying the sentiment of an entire document about a subject, SA detects all references to the given subject, and determines sentiment in each of the references using natural language processing (NLP) techniques. Our sentiment analysis consists of 1) a topic specific...

Automated identification of diverse sentiment types can be beneficial for many NLP systems such as review summarization and public media analysis. In some of these systems there is an option of assigning a sentiment value to a single sentence or a very short text. In this paper we propose a supervised sentiment classification framework which is based on data from Twitter, a popular microbloggin...

We present a statistical parsing framework for sentence-level sentiment classification in this article. Unlike previous works that use syntactic parsing results for sentiment analysis, we develop a statistical parser to directly analyze the sentiment structure of a sentence. We show that complicated phenomena in sentiment analysis (e.g., negation, intensification, and contrast) can be handled t...

The abundance of sentiment-carrying user-generated content renders automated cross-language information monitoring tools crucial for today’s businesses. In order to facilitate cross-language sentiment analysis, we propose to compare the sentiment conveyed by unstructured text across languages through universal star ratings for intended sentiment. We demonstrate that the way natural language rev...

The investigation of investor sentiment and its relation to stock returns, volatility and asset valuation is the subject of considerable debate in many empirical researches. Using indirect sentiment measures, we provide evidence that sentiment levels and changes have important predictive power for stock returns. In addition, we find that most of our sentiment measures cause volatility rather th...

Investors are not all alike and neither is their sentiment. The sentiment of Wall Street strategists is unrelated to the sentiment of individual investors or that of newsletter writers although the sentiment of the last two groups is closely related. Sentiment can be useful for tactical asset allocation. There is a negative relationship between the sentiment of each of the three groups and futu...

In this paper, a new kind of domain specific feature-based heuristic for sentiment analysis of movie reviews using aspect-level is presented. The unsupervised learning technique for sentiment classification is used. The SentiWordNet based scheme using two different linguistic feature selections containing adjectives, adverbs and verbs and n-gram feature extraction is performed. In aspect orient...

This paper documents a new and important cross-sectional determinant of hedge fund returns, their exposures to sentiment risk, measured as beta of fund returns to fluctuations in sentiment proxies. For a large sample of equity-oriented hedge funds, those whose sentiment beta ranks in the top decile subsequently outperform the bottom decile by 0.67% per month, after controlling for fund’s exposu...

As consumers nowadays generate increasingly more content describing their experiences with, e.g., products and brands in various languages, information systems monitoring a universal, languageindependent measure of people’s intended sentiment are crucial for today’s businesses. In order to facilitate sentiment analysis of user-generated content, we propose to map sentiment conveyed by unstructu...