In this paper we consider whether the thematic document clustering approach of Contextual Document Clustering is able to capture the overall sentiment of a cluster of documents. We provide a novel mechanism to determine the sentiment of a cluster based on the latter approach and assess the approach on three data sets formed from the NY Times annotated corpus. We demonstrate that CDC does provid...

The Federal Open Market Committee (FOMC) is a committee within the central banking system of the US and decides on the target rate. Analyzing the positions of its members is a challenge even for experts with a deep knowledge of the financial domain. In our work, we aim at automatically determining opinion groups in transcriptions of the FOMC discussions. We face two main challenges: first, the ...

Studying the relationship between public sentiment and stock prices has been the focus of several studies. This paper analyzes whether the sentiment expressed in Twitter feeds, which discuss selected companies and their products, can indicate their stock price changes. To address this problem, an active learning approach was developed and applied to sentiment analysis of tweet streams in the st...

Sentiment analysis is currently the most actively researched topic in field of natural language processing, however, despite it being such a powerful tool, not very widely used agrarian sector. This research focuses on discovery and scientific literature related to agriculture, provide an overview how where sector which methods are commonly used. article also discusses applications yield benefi...

Figurative language such as irony, sarcasm, and metaphor is considered a significant challenge in sentiment analysis. These figurative devices can sculpt the affect of an utterance and test the limits of sentiment analysis of supposedly literal texts. We explore the effect of figurative language on sentiment analysis. We incorporate the figurative language indicators into the sentiment analysis...