Existing methods for extracting features from Chinese reviews only use simplistic syntactic knowledge, while those for identifying sentiments rely heavily on a semantic dictionary. In this paper, we present a systematic technique for identifying features and sentiments, using both syntactic and statistical analysis. We firstly identify candidate features using a proposed set of common syntactic...

The article describes a prototype system aimed at monitoring attitudes toward any social group. The approach involves web mining and content analysis based on Rectitude Gain category from the Laswell dictionary of political values, extended into shallow predicate rules. The system requires no lexical sentiment resources and no training corpora. It has been designed, implemented and tested in Po...

Automatic sentiment analysis in texts has attracted considerable attention in recent years. Most of the approaches developed to classify texts or sentences as positive or negative rest on a very specific kind of language resource: emotional lexicons. To build these resources, several automatic techniques have been proposed. Some of them are based on dictionaries while others use corpora. One of...

This study adopts data mining methods to analyze the shortand long-term dynamic between news message content and property prices in Spain and the United States. We construct news sentiment indices based on various text mining methods which exhibit remarkable similarities to the respective property prices. Comparing dictionary-based and dynamic approaches, our results indicate that static method...

An important problem in sentiment analysis are inconsistent words. We define an inconsistent word as a sentiment word whose dictionary polarity is reversed by the sentence context in which it occurs. We present a supervised machine learning approach to the problem of inconsistency classification, the problem of automatically distinguishing inconsistent from consistent sentiment words in context...

The aim of this article is to introduce a system that is capable of collecting and analyzing different types of financial data to support traders in their decision-making. Oracle’s Big Data platform Oracle Advanced Analytics was utilized, which extends the Oracle Database with Oracle R, thus providing the opportunity to run embedded R scripts on the database server to speed up data processing. ...

This study investigates the impact of the quality of disclosures of financial reports of the listed firms in Taiwan under her first full adoption of International Financial Reporting Standards (IFRS) in 2013. We select the semi-annual reports of firms in the three main industry sectors of high technology, financial service, and biotechnology representing 80% of the capital market in the first y...

In this paper, we propose a model to analyze sentiment of online stock forum and use the information to predict the stock volatility in the Chinese market. We have labeled the sentiment of the online financial posts and make the dataset public available for research. By generating a sentimental dictionary based on financial terms, we develop a model to compute the sentimental score of each onli...

In light of recent research that has begun to examine the link between textual “big data” and social phenomena such as stock price increases, this chapter takes a novel approach to treating news as big data by proposing the intelligent investment decision-making support model based on opinion mining. In an initial prototype experiment, the researchers first built a stock domain-specific sentime...

Introduction As the use of online and social media increases globally, the need for sentiment analysis tools in multiple languages is critical in order to understand and analyze the vast amount of data that may contain users’ feelings, perceptions, and beliefs. Users from different countries convey their messages in various languages, which may convey different sentiments and cultural connotati...