نتایج جستجو برای: sentiment shock
تعداد نتایج: 116510 فیلتر نتایج به سال:
Recent years, big data has attracted increasing interest. Sentiment analysis from microblog as one kind of big data also receive great attention. Some recent research works are not suitable for sentiment analysis as the result that users prefer to express their feelings in individual ways. In this paper, a framework is proposed to calculate sentiment for aspects of event. Based on some state of...
In this study, we aim to test our hypothesis that confidence scores of sentiment values of tweets aid in classification of sentiment. We used several feature sets consisting of lexical features, emoticons, features based on sentiment scores and combination of lexical and sentiment features. Since our dataset includes confidence scores of real numbers in [0-1] range, we employ regression analysi...
In this paper we describe our current work on Senti–TUT, a novel Italian corpus for sentiment analysis. This resource includes annotations concerning both sentiment and morpho-syntax, in order to make available several possibilities of further exploitation related to sentiment analysis. For what concerns the annotation at sentiment level, we focus on irony and we selected therefore texts on pol...
Lexicon-based approaches to Twitter sentiment analysis are gaining much popularity due to their simplicity, domain independence, and relatively good performance. These approaches rely on sentiment lexicons, where a collection of words are marked with fixed sentiment polarities. However, words’ sentiment orientation (positive, neural, negative) and/or sentiment strengths could change depending o...
We extend beyond healthiness assessment of banks using quantitative financial data by applying textual sentiment analysis. Looking at 10-K annual reports for a large sample of banks in the 2000-2014 period, 52 public bank holding companies that were associated with bank failures during the global financial crisis serve as a natural experiment. Utilizing negative and positive dictionaries propos...
A number of different notions, including subjectivity, have been proposed for distinguishing parts of documents that convey sentiment from those that do not. We propose a new concept, sentiment relevance, to make this distinction and argue that it better reflects the requirements of sentiment analysis systems. We demonstrate experimentally that sentiment relevance and subjectivity are related, ...
Sentiment analysis is the computational study of people’s opinions, as expressed in text. This is an active area of research in Natural Language Processing with many applications in social media. There are two main approaches to sentiment analysis: machine learning and lexicon-based. The machine learning approach uses statistical modelling techniques, whereas the lexicon-based approach uses ‘se...
Natural language processing tools allow the characterization of sentiment--that is, terms expressing positive and negative emotion--in text. Applying such tools to electronic health records may provide insight into meaningful patient or clinician features not captured in coded data alone. We performed sentiment analysis on 2,484 hospital discharge notes for 2,010 individuals from a psychiatric ...
the area of sentiment mining (also called sentiment extraction, opinion mining, opinion extraction, sentiment analysis, etc.) has seen a large increase in academic interest in the last few years. Researchers in the areas of natural language processing, data mining, machine learning, and others have tested a variety of methods of automating the sentiment analysis process. In this research work, ...
Sentiment lexicons for sentiment analysis offer a simple, yet effective way to obtain the prior sentiment information of opinionated words in texts. However, words’ sentiment orientations and strengths often change throughout various contexts in which the words appear. In this paper, we propose a lexicon adaptation approach that uses the contextual semantics of words to capture their contexts i...
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