Purpose. Sentiment analysis and emotion processing are attracting increasing interest in many fields. Computer and information scientists are developing automated methods for sentiment analysis of online text. Most of the research have focused on identifying sentiment polarity or orientation—whether a document, usually product or movie review, carries a positive or negative sentiment. It is tim...

Sentiment analysis with features addition to opinion words has been an appealing area in recent studies. Some research has been conducted for finding relationship between sentiments, topics and temporal sentiment analysis. Nevertheless, Email sentiment analysis received relatively less attention due to the complexity of its structure and indirectness of its language. This paper introduces a sys...

As companies and organizations increasingly rely on on-line, user-supplied data to obtain valuable insights into their operations, sentiment analysis of textual data has proven to be a most valuable resource. To understand how sentiment analysis can be used effectively, it is important to identify what types of sentiment analysis could be employed during the analysis of a given situation. This ...

Tweet sentiment analysis has been an effective and valuable technique in the sentiment analysis domain. As the most widely used approach for tweet sentiment analysis, machine learning algorithms work well on the sentiment classification, just as they have been successfully applied for many other purposes. In this thesis, we conduct a systematic and thorough empirical study on the machine learni...

Public opinions on a topic may change over time. Topic Sentiment change analysis is a new research problem consisting of two main components: (a) mining opinions on a certain topic, and (b) detect significant changes of sentiment of the opinions on the topic and identify possible reasons causing each such change. In this paper, we discuss topic sentiment change analysis using data on the Web. W...

The emergence of big data analytics enables real time news analysis. Such analysis offers the possibility to instantly extract the sentiment conveyed by any newly published, textual information source. This paper investigates the existence of a causal relationship between news sentiment and stock prices. As such, we apply news sentiment analysis for unstructured, textual data to extract sentime...

Sentiment analysis deals with the computational treatment of opinion, sentiment, and subjectivity in text, has attracted a great deal of attention. Sentiment analysis has been widely used across a wide range of domains in recent years, such as information retrieval, question answering systems and social network. This paper presents a new method for improving the semantic knowledge base for sent...

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 ...

We present two NLP components for the Story Cloze Task – dictionary-based sentiment analysis and lexical cohesion. While previous research found no contribution from sentiment analysis to the accuracy on this task, we demonstrate that sentiment is an important aspect. We describe a new approach, using a rule that estimates sentiment congruence in a story. Our sentiment-based system achieves str...