We retrieve news stories and earnings announcements of the S&P 100 constituents from two professional news providers, along with ten macroeconomic indicators. We also gather data from Google Trends about these firms’ assets as an index of retail investors’ attention. Thus, we create an extensive and innovative database that contains precise information with which to analyze the link between new...

Analysts are often interested in how sentiment towards an organization, a product or a particular technology changes over time. Popular methods that process unstructured textual material to automatically detect sentiment based on tagged dictionaries are not capable of fulfilling this task, even when coupled with part-ofspeech tagging, a standard component of most text processing toolkits that d...

AbstractSentiments of words can differ from one corpus to another. Inducing general sentiment lexicons for languages and using them cannot, in general, produce meaningful results different domains. In this paper, we combine contextual supervised information with the semantic representations occurring dictionary. Contexts help us capture domain-specific scores are indicative polarities those wor...

The semantic orientation of terms is fundamental for sentiment analysis in sentence and document levels. Although some Chinese sentiment dictionaries are available, how to predict the orientation of terms automatically is still important. In this paper, we predict the semantic orientation of terms of E-HowNet. We extract many useful features from different sources to represent a Chinese term in...

This paper describes the system that was submitted to SemEval2015 Task 10: Sentiment Analysis in Twitter. We participated in Subtask B: Message Polarity Classification. The task is a message level classification of tweets into positive, negative and neutral sentiments. Our model is primarily a supervised one which consists of well designed features fed into an SVM classifier. In previous runs o...

This study mapped personality based on the newly proposed extraction method from consumers’ textual data and revealed relevance (attention) polarity (affection) of words associated with a specific trait. Furthermore, we illustrate how unique are used to predict consumer’s behavior certain traits. In this study, employed scales Kaggle MBTI Personality dataset examine methodology’s effectiveness,...

In this study, a model of Bayesian Dynamic Stochastic General Equilibrium (DSGE) from Real Business Cycles (RBC) approach with the aim of identifying the factors shaping price bubbles of Tehran Stock Exchange (TSE) was specified. The above-mentioned model was conducted in two scenarios. In the first scenario, the baseline model with sentiment shock was examined. In this model, stock price bubbl...

Abstract The manuscript explores whether and how the strategic context of elections candidate attributes affect campaign sentiment. Studying five decades French presidential elections, it provides first longitudinal test tone outside USA. Thereby, paper examines concerns an increase in negativity due to changes electoral competition. It takes leverage from system, study environment (first vs. s...

Unlabeled data is often used to learn representations which can be used to supplement baseline features in a supervised learner. For example, for text applications where the words lie in a very high dimensional space (the size of the vocabulary), one can learn a low rank “dictionary” by an eigendecomposition of the word co-occurrence matrix (e.g. using PCA or CCA). In this paper, we present a n...