This paper describes an approach to utilizing term weights for sentiment analysis tasks and shows how various term weighting schemes improve the performance of sentiment analysis systems. Previously, sentiment analysis was mostly studied under data-driven and lexicon-based frameworks. Such work generally exploits textual features for fact-based analysis tasks or lexical indicators from a sentim...

In micro-blogging services like Twitter offers a robust outlet for people’s thoughts and feelings it's a colossal ever-growing provider of texts ranging from everyday observations to concerned discussions. This paper contributes to the sphere of sentiment analysis, that aims to extract emotions and sentiment from text. A basic goal is to classify text as expressing either positive or negative f...

The INSRT project short for INterpreted Sentiment in Real Time performs sentiment polarity classi cation on German tweets in real time. Classi cation of sentiment polarity in social media has become an important tool for reputation monitoring and trend analysis. For INSRT, we monitor the list of currently trending topics provided by the Twitter microblogging service and classify the tweets conc...

Sentiment mining is a computational approach used to identify expressions made about topics within a span of text. The blogosphere is a particularly useful corpus for sentiment mining because bloggers express a wide variety of opinions and sentiments in their online journals. Previous works on sentiment identification and extraction have been primarily focused on using machine-learning methods ...

We address the task of sentiment classification identification of the polarity of the subjective document in this paper. We introduces a sentiment classification method called AS LDA. In this model, we assume that words in subjective documents consists of two parts: sentiment element words and auxiliary words which are sampled accordingly from sentiment topics and auxiliary topics. Sentiment el...

Whether or not U.S. women follow the recommended breast cancer screening guidelines is related to the perceived benefits and harms of the procedure. Twitter is a rich source of subjective information containing individuals’ sentiment towards public health interventions/technologies. Using a modified version of Hutto and Gilbert (2014) sentiment classifier, we described the temporal, geospatial,...

This paper investigates the role of published stock recommendations in print and online media as investor sentiment in the near-term German stock market. In line with extant literature on other sentiment measures, vector autoregressions reveal that past stock returns drive today’s sentiment, but not the other way around, and that sentiment is a powerful predictor of itself. In particular, senti...

The DsUniPi team participated in the SemEval 2015 Task#11: Sentiment Analysis of Figurative Language in Twitter. The proposed approach employs syntactical and morphological features, which indicate sentiment polarity in both figurative and non-figurative tweets. These features were combined with others that indicate presence of figurative language in order to predict a fine-grained sentiment sc...

We conclude this report with a system design and proof-of-concept to show how an adaptable hybrid sentiment classification system is able to improve sentiment analysis for organisations. GreenOnline, a service company in the field of customer services, wants to be able to quantify sentiment for organisations precisely, to create new services for organisations. To start with, this sentiment anal...

Purpose This paper aims to analyze the connectedness between Gulf Cooperation Council (GCC) stock market index and cryptocurrencies. It investigates relevant impact of RavenPack COVID sentiment on dynamic indices conventional cryptocurrencies as well their Islamic counterparts during onset COVID-19 crisis. Design/methodology/approach The authors rely methodology Diebold Yilmaz (2012, 2014) cons...