相关论文: Semantic Sentiment Analysis of Twitter Data

As the Covid-19 outbreaks rapidly all over the world day by day and also affects the lives of million, a number of countries declared complete lock-down to check its intensity. During this lockdown period, social media plat-forms have…

Big Data dealing with the social produce predictive correlations for the benefit of brands and web platforms. Beyond "society" and "opinion" for which the text lays out a genealogy, appear the "traces" that must be theorized as…

As microblogging services like Twitter are becoming more and more influential in today's globalised world, its facets like sentiment analysis are being extensively studied. We are no longer constrained by our own opinion. Others opinions…

We present the first comprehensive characterization of the diffusion of ideas on Twitter, studying more than 4000 topics that include both popular and less popular topics. On a data set containing approximately 10 million users and a…

People use the world wide web heavily to share their experience with entities such as products, services, or travel destinations. Texts that provide online feedback in the form of reviews and comments are essential to make consumer…

This paper introduces a large collection of time series data derived from Twitter, postprocessed using word embedding techniques, as well as specialized fine-tuned language models. This data comprises the past five years and captures…

The importance of collective social action in current events is manifest in the Arab Spring and Occupy movements. Electronic social media have become a pervasive channel for social interactions, and a basis of collective social response to…

Social media data can be a very salient source of information during crises. User-generated messages provide a window into people's minds during such times, allowing us insights about their moods and opinions. Due to the vast amounts of…

The Coronavirus pandemic has created complex challenges and adverse circumstances. This research discovers public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential sentiment…

The increasing popularity of the social networking service, Twitter, has made it more involved in day-to-day communications, strengthening social relationships and information dissemination. Conversations on Twitter are now being explored…

In this paper, we present TwiSent, a sentiment analysis system for Twitter. Based on the topic searched, TwiSent collects tweets pertaining to it and categorizes them into the different polarity classes positive, negative and objective.…

The web-based microblogging system Twitter is a very popular altmetrics source for measuring the broader impact of science. In this case study, we demonstrate how problematic the use of Twitter data for research evaluation can be, even…

This paper studies the dynamics of opinion formation and polarization in social media. We investigate whether users' stance concerning contentious subjects is influenced by the online discussions they are exposed to and interactions with…

Social media has become an integral part of modern life, but it has also brought with it the pervasive issue of cyberbullying a serious menace in today's digital age. Cyberbullying, a form of harassment that occurs on social networks, has…

Nowadays, researchers have moved to platforms like Twitter to spread information about their ideas and empirical evidence. Recent studies have shown that social media affects the scientific impact of a paper. However, these studies only…

I explore the relationship between investor emotions expressed on social media and asset prices. The field has seen a proliferation of models aimed at extracting firm-level sentiment from social media data, though the behavior of these…

Sentiment analysis using big data from YouTube videos metadata can be conducted to analyze public opinions on various political figures who represent political parties. This is possible because YouTube has become one of the platforms for…

In this paper we introduce score embedding, a neural network based model to learn interpretable vector representations for words. Score embedding is a supervised method that takes advantage of the labeled training data and the neural…

We propose an analytical framework able to investigate discussions about polarized topics in online social networks from many different angles. The framework supports the analysis of social networks along several dimensions: time, space and…

Extreme weather events driven by climate change, such as wildfires, floods, and heatwaves, prompt significant public reactions on social media platforms. Analyzing the sentiment expressed in these online discussions can offer valuable…