Probabilistic topic models have been widely used for sentiment analysis. However, most of existing topic methods only model the sentiment text, but do not consider the user, who expresses the sentiment, and the item, which the sentiment is expressed on. Since different users may use different sentiment expressions for different items, we argue that it is better to incorporate the user and item ...

Cross-domain sentiment analysis focuses on these problems where the source domain and the target domain are from different domains. However, traditional sentiment classification approaches usually perform poorly to address cross-domain problems. So, this paper proposed a cross-domain sentiment classification method based on extraction of key sentiment sentence. Firstly, based on the observation...

Sentiment Analysis is the most prominent field of Machine Learning and the problem of Natural Language Processing. The aim of sentiment analysis system is to predict the moods and sentiment of public expressed in the form of text from social media. This study exposes the variety of issues for Sentiment analysis related to Machine Learning and Natural Language Processing. The major goal of this ...

We presented the state of art of sentiment analysis which contained about the purpose of sentiment analysis, levels of sentiment analysis and processes that could be used to measure polarity and classify labels. Moreover, brief details about some resources of sentiment analysis are included. Keywords— sentiment, analysis, natural language processing,

As the internet and its applications are growing, E-commerce has become one of its rapid applications. Customers of E-commerce were provided with the opportunity to express their opinion about the product on the web as a text in the form of reviews. In the previous studies, mere founding sentiment from reviews was not helpful to get the exact opinion of the review. In this paper, we have used A...

In most sentiment analysis applications, the sentiment lexicon plays a key role. However, it is hard, if not impossible, to collect and maintain a universal sentiment lexicon for all application domains because different words may be used in different domains. The main existing technique extracts such sentiment words from a large domain corpus based on different conjunctions and the idea of sen...

There is a new generation of emoticons, called emojis, that is increasingly being used in mobile communications and social media. In the past two years, over ten billion emojis were used on Twitter. Emojis are Unicode graphic symbols, used as a shorthand to express concepts and ideas. In contrast to the small number of well-known emoticons that carry clear emotional contents, there are hundreds...

This paper relies on the characteristics of different earnings components to show that sentiment affects both investors’ judgments of expected cash flows and their risk preferences. As suggested in psychology theories, those two effects of sentiment work against each other in the valuation model-optimistic (pessimistic) cash flow judgments come with high (low) discount rates during high (low) s...

With people increasingly using emoticons in written text on the Web in order to express, stress, or disambiguate their sentiment, it is crucial for automated sentiment analysis tools to correctly account for such graphical cues for sentiment. We analyze how emoticons typically convey sentiment and we subsequently propose and evaluate a novel method for exploiting this with a manually created em...

A word's sentiment depends on the domain in which it is used. Computational social science research thus requires sentiment lexicons that are specific to the domains being studied. We combine domain-specific word embeddings with a label propagation framework to induce accurate domain-specific sentiment lexicons using small sets of seed words. We show that our approach achieves state-of-the-art ...