This paper presents a method for sentiment classification, called SESS (SElfSupervised and Syntax-Based method). SESS includes three phases. Firstly, some documents are initially classified based on a sentiment dictionary, and then the sentiments of phrases and documents are iteratively revised. This phase provides some accurately labeled data for the second phase. Secondly, a machine learning ...

Abstract We use Word2vec to develop a financial sentiment dictionary from 3.1 million Chinese-language news articles. Our maps semantically similar words subset of human-expert generated words. In validation tests, our scores the articles consistently with human reading full return association outperforms and subsumes previous Chinese dictionaries, such as direct translations Loughran McDonald’...

Correspondence: [email protected] Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia Full list of author information is available at the end of the article Abstract Studies examining how sentiment on social media varies over time and space appear to produce inconsistent results. Analysing 16.54 million English-language tweets fr...

Sentiment analysis has become an important classification task because a large amount of user-generated content is published over the Internet. Sentiment lexicons have been used successfully to classify the sentiment of user review datasets. More recently, microblogging services such as Twitter have become a popular data source in the domain of sentiment analysis. However, analyzing sentiments ...

This paper explores how information regarding the context can assist in improving sentiment analysis performance. We postulate that context affects sentiment at two different levels: first at the domain level of the comment, and second, at the sentence-structure level. Noting this, we explore three ways of utilizing context in sentiment analysis. First, we study contextual assistance with respe...

Sentiment classification can provide the decision support of social applications such as trend judgment, public opinion monitoring, etc. However, accuracy sentiment for Chinese Weibo is still not satisfactory due to complexity Chinese. In addition, affected by different organisational structure levels, tendency fewer Comments may be judged opposite. To solve problem above, this paper presents a...

Microblogs are an opportunity for scavenging critical information such as sentiments. This information can be used to detect rapidly the sentiment of the crowd towards crises or disasters. It can be used as an effective tool to inform humanitarian efforts, and improve the ways in which informative messages are crafted for the crowd regarding an event. Unique characteristics of microblogs (lack ...

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....

Microblogging services have nowadays become a very popular communication tool among Internet users. Since millions of users share opinions on different aspects of life everyday, microblogging websites are considered as a credible source for exploring both factual and subjective information. This fact has inspired research in the area of automatic sentiment analysis. In this paper we propose an ...

The topic of sentiment analysis in text has been extensively studied in English for the past 30 years. An early, influential work by Cynthia Whissell, the Dictionary of Affect in Language (DAL), allows rating words along three dimensions: pleasantness, activation and imagery. Given the lack of such tools in Spanish, we decided to replicate Whissell’s work in that language. This paper describes ...