Search results for "Sentiment Analysis"
showing 10 items of 46 documents
Examining Competing Entrepreneurial Concerns in a Social Question and Answer (SQA) Platform
2021
This study aims to determine the competing concerns of people interested in startup development and entrepreneurship by using topic modeling and sentiment analysis on a social question-and-answer (SQA) website. Understanding the underlying concerns of startup entrepreneurs is critical to society and economic growth. Therefore, greater scientific support for entrepreneurship remains necessary, including data mining from virtual social communities. In this study, an SQA platform was used to identify the sentiment of thirty concerns of people interested in startup entrepreneurship. Based on topic modeling and sentiment analysis of 18819 inquiries in various forums on an SQA, we identified addi…
Distributed Real-Time Sentiment Analysis for Big Data Social Streams
2014
Big data trend has enforced the data-centric systems to have continuous fast data streams. In recent years, real-time analytics on stream data has formed into a new research field, which aims to answer queries about "what-is-happening-now" with a negligible delay. The real challenge with real-time stream data processing is that it is impossible to store instances of data, and therefore online analytical algorithms are utilized. To perform real-time analytics, pre-processing of data should be performed in a way that only a short summary of stream is stored in main memory. In addition, due to high speed of arrival, average processing time for each instance of data should be in such a way that…
Mining Interpretable Rules for Sentiment and Semantic Relation Analysis Using Tsetlin Machines
2020
Tsetlin Machines (TMs) are an interpretable pattern recognition approach that captures patterns with high discriminative power from data. Patterns are represented as conjunctive clauses in propositional logic, produced using bandit-learning in the form of Tsetlin Automata. In this work, we propose a TM-based approach to two common Natural Language Processing (NLP) tasks, viz. Sentiment Analysis and Semantic Relation Categorization. By performing frequent itemset mining on the patterns produced, we show that they follow existing expert-verified rule-sets or lexicons. Further, our comparison with other widely used machine learning techniques indicates that the TM approach helps maintain inter…
Tecnhiques for sentiment analysis in Twitter: Supervised Learning and SentiStrength
2017
[EN] Sentiment analysis on Twitter offers possibilities of great interest to evaluate the currents of opinion disseminated through this medium. The huge volumes of texts require tools able to automatically process these messages without losing reliability. This paper describes two different types of approaching this problem. The first strategy is based on Supervised Learning processes, developed in the field of artificial intelligence. Its application requires some tools from natural language processing along with a classifed corpus as a starting point. The second approach is based on polarity dictionaries. SentiStrength tool is located in this line. It is increasingly applied to studies of…
How Trump tweets: A comparative analysis of tweets by US politicians
2021
This paper analyses tweets sent from Donald Trump’s Twitter account @realDonaldTrump and contextualises them by contrasting them with several genres (i.e. political and ‘average’ Twitter, blogs, expressive writing, novels, The New York Times and natural speech). Taking common claims about Donald Trump’s language as a starting point, the study focusses on commonalities and differences between his tweets and those by other US politicians. Using the sentiment analysis tool Linguistic Inquiry and Word Count (LIWC) and a principal component analysis, I examine a newly compiled 1.5-million-word corpus of tweets sent from US politicians’ accounts between 2009 and 2018 with a special focus on the q…
A Study on Classification Methods Applied to Sentiment Analysis
2013
Sentiment analysis is a new area of research in data mining that concerns the detection of opinions and/or sentiments in texts. This work focuses on the application and the comparison of three classification techniques over a text corpus composed of reviews of commercial products in order to detect opinions about them. The chosen domain is about "perfumes", and user opinions composing the corpus are written in Italian language. The proposed approach is completely data-driven: a Term Frequency / Inverse Document Frequency (TFIDF) terms selection procedure has been applied in order to make computation more efficient, to improve the classification results and to manage some issues related to t…
How News Affect the Trading Behavior of Different Categories of Investors in a Financial Market
2012
We investigate the trading behavior of a large set of single investors trading the highly liquid Nokia stock over the period 2003-2008 with the aim of determining the relative role of endogenous and exogenous factors that may affect their behavior. As endogenous factors we consider returns and volatility, whereas the exogenous factors we use are the total daily number of news and a semantic variable based on a sentiment analysis of news. Linear regression and partial correlation analysis of data show that different categories of investors are differently correlated to these factors. Governmental and non profit organizations are weakly sensitive to news and returns or volatility, and, typica…
Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics
2015
The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users' behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and dai…
Towards Psychologically based Personalised Modelling of Emotions Using Associative Classifiers
2016
Learning environments, among other user-centred applications, are excellent candidates to trial Computational Emotions and their algorithms to enhance user experience and to expand the system usability. However, this was not feasible because of the paucity in affordable consumer technologies that support the requirements of systems with advanced cognitive capabilities. Microsoft Kinect provides an accessible and affordable technology that can enable cognitive features such as facial expressions extraction and emotions detection. However, it comes with its own additional challenges, such as the limited number of extracted Animation Units (AUs). This paper presents a new approach that attempt…
Towards a fuzzy-linguistic based social network sentiment-expression system
2015
Liking allows users of Social Networks, blogs and online magazines to express their support of posts and artifacts by a simple click. Such function is very popular but lacks semantic power, and some platforms have augmented it by allowing to choose a pictographic depiction corresponding to a feeling. What is gained in depth is lost in simplicity, and the wide acceptance liking has enjoyed did not carried to the sentiment version. We outline a sentiment-expression hybrid system based on textual analysis and linguistic fuzzy Markov chains overcoming the intrinsic limitations of liking without burdening the user with complex choices.