News Sentiment Analysis

• URL: http://arxiv.org/abs/2007.02238v1
• Date: Sun, 5 Jul 2020 05:15:35 GMT
• Title: News Sentiment Analysis
• Authors: Antony Samuels, John Mcgonical
• Abstract summary: This paper presents a lexicon-based approach for sentiment analysis of news articles. The experiments have been performed on BBC news data set, which expresses the applicability and validation of the adopted approach.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Modern technological era has reshaped traditional lifestyle in several domains. The medium of publishing news and events has become faster with the advancement of Information Technology. IT has also been flooded with immense amounts of data, which is being published every minute of every day, by millions of users, in the shape of comments, blogs, news sharing through blogs, social media micro-blogging websites and many more. Manual traversal of such huge data is a challenging job, thus, sophisticated methods are acquired to perform this task automatically and efficiently. News reports events that comprise of emotions - good, bad, neutral. Sentiment analysis is utilized to investigate human emotions present in textual information. This paper presents a lexicon-based approach for sentiment analysis of news articles. The experiments have been performed on BBC news data set, which expresses the applicability and validation of the adopted approach.

Related papers

• Adapting Fake News Detection to the Era of Large Language Models [48.5847914481222]
  We study the interplay between machine-(paraphrased) real news, machine-generated fake news, human-written fake news, and human-written real news. Our experiments reveal an interesting pattern that detectors trained exclusively on human-written articles can indeed perform well at detecting machine-generated fake news, but not vice versa.
  arXiv  Detail & Related papers  (2023-11-02T08:39:45Z)
• Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection [50.07850264495737]
  "Prompt-and-Align" (P&A) is a novel prompt-based paradigm for few-shot fake news detection. We show that P&A sets new states-of-the-art for few-shot fake news detection performance by significant margins.
  arXiv  Detail & Related papers  (2023-09-28T13:19:43Z)
• ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media [74.93847489218008]
  We present a novel task, identifying manipulation of news on social media, which aims to detect manipulation in social media posts and identify manipulated or inserted information. To study this task, we have proposed a data collection schema and curated a dataset called ManiTweet, consisting of 3.6K pairs of tweets and corresponding articles. Our analysis demonstrates that this task is highly challenging, with large language models (LLMs) yielding unsatisfactory performance.
  arXiv  Detail & Related papers  (2023-05-23T16:40:07Z)
• Types of Approaches, Applications and Challenges in the Development of Sentiment Analysis Systems [0.0]
  Sentiment analysis is one of the important applications of natural language processing and machine learning. Millions of comments are recorded daily and it creates a huge volume of unstructured text data.
  arXiv  Detail & Related papers  (2023-03-09T15:18:34Z)
• A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions [4.257210316104905]
  The paper presents a systematic literature review of the existing literature published between 2005 to 2021 in text-based emotion detection. This review has meticulously examined 63 research papers from IEEE, Science Direct, Scopus, and Web of Science databases to address four primary research questions. An overview of various emotion models, techniques, feature extraction methods, datasets, and research challenges with future directions has also been represented.
  arXiv  Detail & Related papers  (2022-04-26T15:20:00Z)
• Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation [105.20743048379387]
  We propose a novel framework for generating training examples informed by the known styles and strategies of human-authored propaganda. Specifically, we perform self-critical sequence training guided by natural language inference to ensure the validity of the generated articles. Our experimental results show that fake news detectors trained on PropaNews are better at detecting human-written disinformation by 3.62 - 7.69% F1 score on two public datasets.
  arXiv  Detail & Related papers  (2022-03-10T14:24:19Z)
• A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks [3.42658286826597]
  Fake news detection is one of the most analyzed and prominent areas of research. Facebook, Reddit, WhatsApp, YouTube, and other social applications are gradually gaining attention in this emerging field. This review primarily deals with multi-modal fake news detection techniques that include images, videos, and their combinations with text.
  arXiv  Detail & Related papers  (2021-11-23T16:02:28Z)
• Fake News Detection: Experiments and Approaches beyond Linguistic Features [0.0]
  Credibility information and metadata associated with the news article have been used for improved results. The experiments also show how modelling justification or evidence can lead to improved results.
  arXiv  Detail & Related papers  (2021-09-27T10:00:44Z)
• A Study of Fake News Reading and Annotating in Social Media Context [1.0499611180329804]
  We present an eye-tracking study, in which we let 44 lay participants to casually read through a social media feed containing posts with news articles, some of which were fake. In a second run, we asked the participants to decide on the truthfulness of these articles. We also describe a follow-up qualitative study with a similar scenario but this time with 7 expert fake news annotators.
  arXiv  Detail & Related papers  (2021-09-26T08:11:17Z)
• Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News [57.9843300852526]
  We introduce the more realistic and challenging task of defending against machine-generated news that also includes images and captions. To identify the possible weaknesses that adversaries can exploit, we create a NeuralNews dataset composed of 4 different types of generated articles. In addition to the valuable insights gleaned from our user study experiments, we provide a relatively effective approach based on detecting visual-semantic inconsistencies.
  arXiv  Detail & Related papers  (2020-09-16T14:13:15Z)
• A Deep Learning Approach for Automatic Detection of Fake News [47.00462375817434]
  We propose two models based on deep learning for solving fake news detection problem in online news contents of multiple domains. We evaluate our techniques on the two recently released datasets, namely FakeNews AMT and Celebrity for fake news detection.
  arXiv  Detail & Related papers  (2020-05-11T09:07:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.