相关论文: Sentiment Analysis of Economic Text: A Lexicon-Bas…
Sentiment analysis on software engineering (SE) texts has been widely used in the SE research, such as evaluating app reviews or analyzing developers sentiments in commit messages. To better support the use of automated sentiment analysis…
The sentiment analysis task has various applications in practice. In the sentiment analysis task, words and phrases that represent positive and negative emotions are important. Finding out the words that represent the emotion from the text…
Sentiment analysis possesses the potential of diverse applicability on digital platforms. Sentiment analysis extracts the polarity to understand the intensity and subjectivity in the text. This work uses a lexicon-based method to perform…
Words play a central role in how we express ourselves. Lexicons of word-emotion associations are widely used in research and real-world applications for sentiment analysis, tracking emotions associated with products and policies, studying…
The goal of this paper is to evaluate the informational content of sentiment extracted from news articles about the state of the economy. We propose a fine-grained aspect-based sentiment analysis that has two main characteristics: 1) we…
Lexicons of word-emotion associations are widely used in research and real-world applications. As part of my research, I have created several such lexicons (e.g., the NRC Emotion Lexicon). This paper outlines some practical and ethical…
Sentiment analysis aims to uncover emotions conveyed through information. In its simplest form, it is performed on a polarity basis, where the goal is to classify information with positive or negative emotion. Recent research has explored…
Lexicon-based sentiment analysis (SA) in finance leverages specialized, manually annotated lexicons created by human experts to extract sentiment from financial texts. Although lexicon-based methods are simple to implement and fast to…
This paper explores the task of identifying the overall sentiment expressed towards volitional entities (persons and organizations) in a document -- what we refer to as Entity-Level Sentiment Analysis (ELSA). While identifying sentiment…
Sentiment lexicons are instrumental for sentiment analysis. One can use a set of sentiment words provided in a sentiment lexicon and a lexicon-based classifier to perform sentiment classification. One major issue with this approach is that…
Newsletters and social networks can reflect the opinion about the market and specific stocks from the perspective of analysts and the general public on products and/or services provided by a company. Therefore, sentiment analysis of these…
Current approaches to cross-lingual sentiment analysis try to leverage the wealth of labeled English data using bilingual lexicons, bilingual vector space embeddings, or machine translation systems. Here we show that it is possible to use a…
Textual sentiment analysis and emotion detection consists in retrieving the sentiment or emotion carried by a text or document. This task can be useful in many domains: opinion mining, prediction, feedbacks, etc. However, building a general…
Mining financial text documents and understanding the sentiments of individual investors, institutions and markets is an important and challenging problem in the literature. Current approaches to mine sentiments from financial texts largely…
The use of robo-readers to analyze news texts is an emerging technology trend in computational finance. In recent research, a substantial effort has been invested to develop sophisticated financial polarity-lexicons that can be used to…
The task of sentiment analysis of reviews is carried out using manually built / automatically generated lexicon resources of their own with which terms are matched with lexicon to compute the term count for positive and negative polarity.…
The Efficient Market Hypothesis (EMH) highlights the essence of financial news in stock price movement. Financial news comes in the form of corporate announcements, news titles, and other forms of digital text. The generation of insights…
Social media platforms and online forums generate rapid and increasing amount of textual data. Businesses, government agencies, and media organizations seek to perform sentiment analysis on this rich text data. The results of these…
Sentiment analysis is one of the most widely used techniques in text analysis. Recent advancements with Large Language Models have made it more accurate and accessible than ever, allowing researchers to classify text with only a plain…
Rapid increase in internet users along with growing power of online review sites and social media has given birth to sentiment analysis or opinion mining, which aims at determining what other people think and comment. Sentiments or Opinions…