The sentiment classification performance relies on high-quality sentiment resources. However, these resources are imbalanced in different languages. Cross-language sentiment classification (CLSC) can leverage the rich resources in one language (source language) for sentiment classification in a resource-scarce language (target language). Bilingual embeddings could eliminate the semantic gap bet...

Sentiment analysis means to classify a given review document into positive or negative polar document. Sentiment analysis research has been increased tremendously in recent times due to its large number of applications in the industry and academia. Sentiment analysis models can be used to determine the opinion of the user towards any entity or product. E-commerce companies can use sentiment ana...

nahj-ol balaghah is of many values in several aspects. the objective of this paper is to introduce the literary arts involved in this sacred book written the master of eloquence and declamation, the commander of the faithful, imam ali ibn-abi talib (may god honor him) the present paper intends to clarify one of the literary elements cast in most of the sermons and orations in n ahj-ol balaghah ...

We describe a state-of-the-art sentiment analysis system that detects (a) the sentiment of short informal textual messages such as tweets and SMS (message-level task) and (b) the sentiment of a word or a phrase within a message (term-level task). The system is based on a supervised statistical text classification approach leveraging a variety of surfaceform, semantic, and sentiment features. Th...

The question about whether investor sentiment correlates with their corporate investment is often examined in the literature. Thus, based on behavioral finance theory, this paper aims to investigate whether investor sentiment in stock market has an impact on corporate investment in listed Chinese firms, where the mutual fund flows is taken as the proxy variable of investor sentiment for stocks....

Sentiment analysis is the Natural Language Processing (NLP) task dealing with the detection and classification of sentiments in texts. While some tasks deal with identifying presence of sentiment in text (Subjectivity analysis), other tasks aim at determining the polarity of the text categorizing them as positive, negative and neutral. Whenever there is presence of sentiment in text, it has a s...

Mike Thelwall, Kevan Buckley, Georgios Paltoglou Statistical Cybermetrics Research Group, School of Technology, University of Wolverhampton, Wulfruna Street, Wolverhampton WV1 1SB, UK. E-mail: [email protected], [email protected], [email protected] Tel: +44 1902 321470 Fax: +44 1902 321478 Sentiment analysis is concerned with the automatic extraction of sentiment-related information ...

With the rise of social media, sentiment analysis has become a popular research field in recent years. Sentiment dictionaries are vital for this task; however, there are few available in Chinese. Translated English sentiment dictionaries are often inaccurate or lacking vocabulary. Moreover, many dictionaries provide only binary polarity values or no values at all. In this paper, we integrate se...

Sentiment analysis systems can benefit from the translation of sentiment information. We present a novel, graph-based approach using SimRank, a well-established graph-theoretic algorithm, to transfer sentiment information from a source language to a target language. We evaluate this method in comparison with semantic orientation using pointwise mutual information (SO-PMI), an established unsupe...

This paper attempts to identify the importance of sentiment words in financial reports on financial risk. By using a financespecific sentiment lexicon, we apply regression and ranking techniques to analyze the relations between sentiment words and financial risk. The experimental results show that, based on the bag-of-words model, models trained on sentiment words only result in comparable perf...