Related papers: Inferring Political Preferences from Twitter
As the popularity and reach of social networks continue to surge, a vast reservoir of opinions and sentiments across various subjects inundates these platforms. Among these, X social network (formerly Twitter) stands as a juggernaut,…
Sentiment analysis is a natural language processing task that aims to identify and extract the emotional aspects of a text. However, many existing sentiment analysis methods primarily classify the overall polarity of a text, overlooking the…
This paper studies users' perception regarding a controversial product, namely self-driving (autonomous) cars. To find people's opinion regarding this new technology, we used an annotated Twitter dataset, and extracted the topics in…
Twitter sentiment analysis, which often focuses on predicting the polarity of tweets, has attracted increasing attention over the last years, in particular with the rise of deep learning (DL). In this paper, we propose a new task:…
Several messages express opinions about events, products, and services, political views or even their author's emotional state and mood. Sentiment analysis has been used in several applications including analysis of the repercussions of…
Social media have been widely exploited to detect and gather relevant information about opinions and events. However, the relevance of the information is very subjective and rather depends on the application and the end-users. In this…
Understanding who blames or supports whom in news text is a critical research question in computational social science. Traditional methods and datasets for sentiment analysis are, however, not suitable for the domain of political text as…
Anticipating audience reaction towards a certain piece of text is integral to several facets of society ranging from politics, research, and commercial industries. Sentiment analysis (SA) is a useful natural language processing (NLP)…
On December 7, 2020, Ghanaians participated in the polls to determine their president for the next four years. To gain insights from this presidential election, we conducted stance analysis (which is not always equivalent to sentiment…
In today's world, everyone is expressive in some way, and the focus of this project is on people's opinions about rising electricity prices in United Kingdom and India using data from Twitter, a micro-blogging platform on which people post…
In recent years, sentiment analysis and emotion classification are two of the most abundantly used techniques in the field of Natural Language Processing (NLP). Although sentiment analysis and emotion classification are used commonly in…
In this paper, we look at a database of tweets sorted by various keywords that could indicate the users sentiment towards covid vaccines. With social media becoming such a prevalent source of opinion, sorting and ranking tweets that hold…
Regressions trained to predict the future activity of social media users need rich features for accurate predictions. Many advanced models exist to generate such features; however, the time complexities of their computations are often…
Twitter is a well-known microblogging social site where users express their views and opinions in real-time. As a result, tweets tend to contain valuable information. With the advancements of deep learning in the domain of natural language…
Sentiment Analysis (SA) or opinion mining is analysis of emotions and opinions from any kind of text. SA helps in tracking peoples viewpoints and it is an important factor when it comes to social media monitoring product and brand…
This study explores the intersection of fashion trends and social media sentiment through computational analysis of Twitter data using the T4SA (Twitter for Sentiment Analysis) dataset. By applying natural language processing and machine…
Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as "thumbs up" or "thumbs down". To determine this sentiment polarity, we propose a novel machine-learning…
Social media and social networking sites have become a global pinboard for exposition and discussion of news, topics, and ideas, where social media users often update their opinions about a particular topic by learning from the opinions…
Data extracted from social media platforms, such as Twitter, are both large in scale and complex in nature, since they contain both unstructured text, as well as structured data, such as time stamps and interactions between users. A key…
Social media have substantially altered the way brands and businesses advertise: Online Social Networks provide brands with more versatile and dynamic channels for advertisement than traditional media (e.g., TV and radio). Levels of…