Sentiment analysis over social streams offers governments and organisations a fast and effective way to monitor the publics’ feelings towards policies, brands, business, etc. In this paper we present SentiCircles, a platform that captures feedback from social media conversations and applies contextual and conceptual sentiment analysis models to extract and summarise sentiment from these convers...

Most of existing work learn sentiment-specific word representation for improving Twitter sentiment classification, which encoded both n-gram and distant supervised tweet sentiment information in learning process. They assume all words within a tweet have the same sentiment polarity as the whole tweet, which ignores the word its own sentiment polarity. To address this problem, we propose to lear...

Various topic models have been developed for sentiment analysis tasks. But the simple topic-sentiment mixture assumption prohibits them from finding fine-grained dependency between topical aspects and sentiments. In this paper, we build a Hidden Topic Sentiment Model (HTSM) to explicitly capture topic coherence and sentiment consistency in an opinionated text document to accurately extract late...

This study reconciles inconsistent evidence on the sentiment-price relation in prior studies by explicitly considering the effects of sentiment on both investor judgments and risk preferences. Using the uncertainty in accounting information, I am able to disentangle these two effects of sentiment and investigate the causes of the variations in the sentimentprice relation. The results show that,...

Web reviews have been intensively studied in argumentation-related tasks such as sentiment analysis. However, due to their focus on content-based features, many sentiment analysis approaches are effective only for reviews from those domains they have been specifically modeled for. This paper puts its focus on domain independence and asks whether a general model can be found for how people argue...

In this paper we investigate whether stock market overpricing leads to aggregate (real) inefficiencies. We first investigate a standard dynamic contracting model of investment subject to financing constraints. We show that stock market mispricing will have two robust effects on welfare: on the one hand it will distort investment decisions and lead to inefficiencies. On the other hand it will al...

We propose a novel approach to sentiment analysis for a low resource setting. The intuition behind this work is that sentiment expressed towards an entity, targeted sentiment, may be viewed as a span of sentiment expressed across the entity. This representation allows us to model sentiment detection as a sequence tagging problem, jointly discovering people and organizations along with whether t...

This paper introduced a clustering-based Chinese sentiment analysis approach which is a new method to sentiment analysis appropriated for short text such as Sina Weibo. By building Sentiment Sequence from Weibo text, we apply the Longest Common Sequence algorithms to measure the sentiment different from two Sentiment Sequence, and K-Medoids clustering method to break a mass of Sentiment Sequenc...

Public opinions on a topic may change over time. Topic Sentiment change analysis is a new research problem consisting of two main components: (a) mining opinions on a certain topic, and (b) detect significant changes of sentiment of the opinions on the topic and identify possible reasons causing each such change. In this paper, we discuss topic sentiment change analysis using data on the Web. W...

A key point in Sentiment Analysis is to determine the polarity of the sentiment implied by a certain word or expression. In basic Sentiment Analysis systems this sentiment polarity of the words is accounted and weighted in different ways to provide a degree of positivity/negativity. Currently words are also modelled as continuous dense vectors, known as word embeddings, which seem to encode int...