Sentiment is a high-level abstraction, and it challenging task to accurately extract sentimental features from visual contents due the “affective gap”. Previous works focus on extracting more concrete of individual objects by introducing saliency detection or instance segmentation into their models, neglecting interaction among objects. Inspired observation that can impact sentiment images, we ...

Online social networks usage are pervasive now a days. Mining the text present in online social networks will be useful for predictive analytic. Predicting information from unstructured data present in the social networks is a challenging research problem. Extracting, identifying or otherwise characterizing the sentiment content of the text unit using statistics and machine learning methods are...

Sentiment analysis is a wide area with great potential and many research directions. One direction is stance detection, which is somewhat similar to sentiment analysis. We supplement stance detection dataset with sentiment annotation and explore the similarities of these tasks. We show that stance detection and sentiment analysis can be mutually beneficial by using gold label for one task as fe...

Text on the web has become a valuable source for mining and analyzing user opinions on any topic. Non-native English speakers heavily support the growing use of Network media especially in Chinese. Many sentiment analysis studies have shown that a polarity lexicon can effectively improve the classification consequences. Social media, where users spontaneously generated content have become impor...

We construct investor sentiment of UK stock market using the procedure of principal component analysis. Using sentiment-augmented EGARCH component model, we analyse the impacts of sentiment on market excess return, the permanent component of market volatility and the transitory component of market volatility. Bullish sentiment leads to higher market excess return while bearish sentiment leads 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...

Microblog sentiment analysis aims at discovering the users’ attitude of hot events. Difficulties of microblog sentiment analysis lie on the short length of text and lack of labeled corpora. Para2vec based on deep learning attracts people's attention recently and the low-dimensional paragraph vectors trained by para2vec get excellent results on sentiment analysis. But when applying it for sentim...

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...

Recent years, big data has attracted increasing interest. Sentiment analysis from microblog as one kind of big data also receive great attention. Some recent research works are not suitable for sentiment analysis as the result that users prefer to express their feelings in individual ways. In this paper, a framework is proposed to calculate sentiment for aspects of event. Based on some state of...