Search results for "Sentiment analysis"
showing 10 items of 46 documents
Body Gestures and Spoken Sentences: A Novel Approach for Revealing User’s Emotions
2017
In the last decade, there has been a growing interest in emotion analysis research, which has been applied in several areas of computer science. Many authors have con- tributed to the development of emotion recognition algorithms, considering textual or non verbal data as input, such as facial expressions, gestures or, in the case of multi-modal emotion recognition, a combination of them. In this paper, we describe a method to detect emotions from gestures using the skeletal data obtained from Kinect-like devices as input, as well as a textual description of their meaning. The experimental results show that the correlation existing between body movements and spoken user sentence(s) can be u…
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…
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…
Towards Bankruptcy Prediction: Deep Sentiment Mining to Detect Financial Distress from Business Management Reports
2018
Due to their disclosure required by law, business management reports have become publicly available for a large number of companies, and these reports offer the opportunity to assess the financial health or distress of a company, both quantitatively from the balance sheets and qualitatively from the text. In this paper, we analyze the potential of deep sentiment mining from the textual parts of business management reports and aim to detect signals for financial distress. We (1) created the largest corpus of business reports analyzed qualitatively to date, (2) defined a non-trivial target variable based on the so-called Altman Z-score, (3) developed a filtering of sentences based on class-co…
A Lexicon-based Approach for Sentiment Classification of Amazon Books Reviews in Italian Language
2016
We present a system aimed at the automatic classification of the sentiment orientation expressed into book reviews written in Italian language. The system we have developed is found on a lexicon-based approach and uses NLP techniques in order to take into account the linguistic relation between terms in the analyzed texts. The classification of a review is based on the average sentiment strenght of its sentences, while the classification of each sentence is obtained through a parsing process inspecting, for each term, a window of previous items to detect particular combinations of elements giving inversions or variations of polarity. The score of a single word depends on all the associated …
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…
Reactions and countermeasures of medical oncologists towards the incoming COVID-19 pandemic: A whatsapp messenger-based report from the Italian colle…
2020
Background This descriptive, unplanned investigation has been undertaken to report reactions, attitudes and countermeasures which have been put in place and implemented by medical oncology units facing the COVID-19 outbreak in Southern Italy. Materials and methods Data have been retrospectively obtained from the time-related analysis of conversations via a WhatsApp messenger-based group chat between the medical directors belonging to the Italian College of Medical Oncology Directors. Overall number, intensity and time trend of conversations related to reactions during the 4 weeks of observation related to the crucial events which occurred between 24 February and 28 March, 2020 2020 are incl…
Detecting Emotions in Comments on Forums
2014
The paper presents one of the most important issues in Natural Language Processing (NLP), emotion identification and classification to implement a computational technology based on existing resources, open-source or freely available for research purposes. Furthermore, we are interested to use it for establishing Gold standards in sentiment analysis area, such as SentiWordNet. In this sense, we propose to recognize and classify the emotions (sentiments) of the public consumer from the written texts which appeared on the various Forums. We analyse the writing style which refers to how consumers construct sentences together when they write comments to indicate their passion about an entity (pe…
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…
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…