Affective computing is the calculation about sentiment, sentiment generated and the aspects of affecting the sentiment. However, the different factors often cause the uncertainty of sentiment expression of the users. Today twitter as the information media of real-time and timely has become better sentiment expression vector for users themselves. Therefore, in allusion to the diversity of sentim...

News can be good or bad, but it is seldom neutral. Although full comprehension of natural language text remains well beyond the power of machines, the statistical analysis of relatively simple sentiment cues can provide a surprisingly meaningful sense of how the latest news impacts important entities. Here we demonstrate our large-scale sentiment analysis system for news and blog entities built...

Despite the widespread use of sentiment analysis by many disciplines, it has been a largely underused tool in educational contexts. The purpose of this paper is to explore some potential uses for sentiment analysis in educational settings and to present a sample study using the approach. Using sentiment analysis, we compare the “friendliness” of two educational technology conferences and use th...

In this paper we propose a method that uses corpora where phrases are annotated as Positive, Negative, Objective and Neutral, to achieve new sentiment resources involving words dictionaries with their associated polarity. Our method was created to build sentiment words inventories based on sentisemantic evidences obtained after exploring text with annotated sentiment polarity information. Throu...

We examine the explanatory and predictive power of fundamental macroeconomic and behavioral factors with regards to stock returns of the Dow Jones Industrials Index. With a novel sentiment dataset from over 3.6 million Reuters news articles, we nd signi cant correlations between Reuters sentiment and stock returns. We show with vector autoregression and error correction models that Reuters sent...

Two typical approaches to sentiment analysis are lexicon look up and machine learning. Even though recent studies have shown that machine learning approaches in general outperform the lexicon look up approaches, completely ignoring the knowledge encoded in sentiment lexicons may not be optimal. We present an alternative method that incorporates sentiment lexicons as prior knowledge with machine...

Treating sentiment analysis as a classification problem has proven extremely useful, but it misses the blended, continuous nature of sentiment expression in natural language. Using data from the Experience Project, we study texts as distributions over sentiment categories. Analysis of the document collection shows the texts contain blended sentiment information substantially different from a ca...

Psychological factors are commonly believed to play a role on cyclical economic fluctuations, but they are typically omitted from state-of-the-art macroeconomic models. This paper introduces “sentiment” in a medium-scale DSGE model of the U.S. economy and tests the empirical contribution of sentiment shocks to business cycle fluctuations. The assumption of rational expectations is relaxed. The ...

With the spurt of online user-generated contents on web, sentiment analysis has become a very active research issue in data mining and natural language processing. As the most important indicator of sentiment, sentiment words which convey positive and negative polarity are quite instrumental for sentiment analysis. However, most of the existing methods for identifying polarity of sentiment word...

The advent of social media has shaken the very foundations of how we share information, with Twitter, Facebook, and Linkedin among many well-known social networking platforms that facilitate information generation and distribution. However, the maximum 140-character restriction in Twitter encourages users to (sometimes deliberately) write somewhat informally in most cases. As a result, machine ...