Rapid increase in the volume of sentiment rich social media on the web has resulted in an increased interest among researchers regarding Sentimental Analysis and opinion mining. However, with so much social media available on the web, sentiment analysis is now considered as a big data task. Hence the conventional sentiment analysis approaches fails to efficiently handle the vast amount of senti...

Sentiment analysis aims at extracting sentiment embedded mainly in text reviews. The prevalence of semantic web technologies has encouraged users of the web to become authors as well as readers. People write on a wide range of topics. These writings embed valuable information for organizations and industries. This paper introduces a novel framework for sentiment detection in Arabic tweets. The ...

In this study, an automatic classification method based on the sentiment polarity of text is proposed. This method uses two sentiment dictionaries from different sources: the Chinese sentiment dictionary CSWN that integrates Chinese WordNet with SentiWordNet, and the sentiment dictionary obtained from a training corpus labeled with sentiment polarities. In this study, the sentiment polarity of ...

This paper presents a novel algorithm to compute sentiment orientation of Chinese sentiment word. The algorithm uses ideograms which are a distinguishing feature of Chinese language. The proposed algorithm can be applied to any sentiment classification scheme. To compute a word’s sentiment orientation using the proposed algorithm, only the word itself and a precomputed character ontology is req...

This paper describes our approach for SemEval-2017 Task 4 Sentiment Analysis in Twitter (SAT). Its five subtasks are divided into two categories: (1) sentiment classification, i.e., predicting topic-based tweet sentiment polarity, and (2) sentiment quantification, that is, estimating the sentiment distributions of a set of given tweets. We build a convolutional sentence classification system fo...

Sentiment polarity analysis on the microblogging hot topic can better understand the Internet user's attitude and tendency toward a specific event, so that government can effectively guide the public opinion. Different from other methods, the sentiment polarity analysis method put forward in this paper gives full consideration to the expression characteristics of microblogging, a particular net...

Sentiment analysis attempts to extract the author’s sentiments or opinions from unstructured text. Unlike approaches based on rules, a machine learning approach holds the promise of learning robust, highcoverage sentiment classifiers from labeled examples. However, people tend to use different ways to express the same sentiment due to the richness of natural language. Therefore, each sentiment ...

Sentiment analysis has been shown to be a useful tool for quantitative analysis in the world of finance. Researchers have shown that the sentiment picked up from the news media can be correlated with movement of the stock market. Here we use the Harvard General Inquirer to determine the sentiment present in Reuter’s articles. After first generating positive and negative sentiment data we use th...

Sentiment analysis is an emerging research field. One of the major tasks of sentiment analysis is building sentiment lexicons and calculating their scores, which is an essential job that provides “material” for all sentiment analysis problems. In this paper, we propose a fuzzy language computation by taking linguistic context into account to provide an effective method for computing the sentime...

Sentiment classification attempts to identify the sentiment polarity of a given text as either positive or negative. Much of the work has been focused on Sentiment classification using machine learning methods in last decades. Analyzing and predicting the polarity of the sentiment plays an important role in decision making. Related work about hybrid methods contributing to sentiment classificat...