Huge volumes of opinion-rich data is user-generated in social media at an unprecedented rate, easing the analysis of individual and public sentiments. Sentiment analysis has shown to be useful in probing and understanding emotions, expressions and attitudes in the text. However, the distinct characteristics of social media data present challenges to traditional sentiment analysis. First, social...

In order to create video recommender tools that take sentiment into account, we conducted a study to get more information on how people describe sentiment in a video and how we can automatically detect sentiment in a video by making use of a video labeling game. First, we did a experiment on how people classified the sentiment of a video fragment by manual explicit sentiment judgments. Secondly...

a r t i c l e i n f o Keywords: Investor sentiment Stock returns Chinese A-share stock market Firm characteristics Penalized panel quantile regression model This paper employs the panel quantile regression model to study the nonlinear effect of investor sentiment on monthly stock returns in the Chinese A-share stock market. The findings show that the influence of investor sentiment is significa...

Internet has opened the new doors for information exchange and the growth of social media has created unprecedented opportunities for citizens to publicly raise their opinions, but it has serious bottlenecks when it comes to do analysis of these opinions. Even urgency to gain a real time understanding of citizens concerns has grown very rapidly. Since, the viral nature of social media which is ...

Sentiment in social media is increasingly considered as an important resource for customer segmentation, market understanding, and tackling other socio-economic issues. However, sentiment in social media is difficult to measure since user-generated content is usually short and informal. Although many traditional sentiment analysis methods have been proposed, identifying slang sentiment words re...

Following the rapid development of social media, sentiment analysis has become an important social media mining technique. The performance of automatic sentiment analysis primarily depends on feature selection and sentiment classification. While information gain (IG) and support vector machines (SVM) are two important techniques, few studies have optimized both approaches in sentiment analysis....

Sentiment analysis aims to automatically uncover the underlying attitude that we hold towards an entity. The aggregation of these sentiments over a population represents opinion polling and has numerous applications. Current text-based sentiment analysis relies on the construction of dictionaries and machine learning models that learn sentiment from large text corpora. Sentiment analysis from t...

Sentiment analysis deals with the computational treatment of opinion, sentiment, and subjectivity in text, has attracted a great deal of attention. Sentiment analysis has been widely used across a wide range of domains in recent years, such as information retrieval, question answering systems and social network. This paper presents a new method for improving the semantic knowledge base for sent...

The Baker and Wurgler (2006) sentiment index purports to measure irrational investor sentiment, while the University of Michigan Consumer Sentiment Index is designed to largely reflect fundamentals. Removing this fundamental component from the Baker and Wurgler index creates an index of investor sentiment that may better capture irrational sentiment. This new index predicts returns better than ...

The emergence of big data analytics enables real time news analysis. Such analysis offers the possibility to instantly extract the sentiment conveyed by any newly published, textual information source. This paper investigates the existence of a causal relationship between news sentiment and stock prices. As such, we apply news sentiment analysis for unstructured, textual data to extract sentime...