ReDDIT: Regret Detection and Domain Identification from Text
- URL: http://arxiv.org/abs/2212.07549v1
- Date: Wed, 14 Dec 2022 23:41:57 GMT
- Title: ReDDIT: Regret Detection and Domain Identification from Text
- Authors: Fazlourrahman Balouchzahi, Sabur Butt, Grigori Sidorov, Alexander
Gelbukh
- Abstract summary: We present a novel dataset of Reddit texts that have been classified into three classes: Regret by Action, Regret by Inaction, and No Regret.
Our findings show that Reddit users are most likely to express regret for past actions, particularly in the domain of relationships.
- Score: 62.997667081978825
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we present a study of regret and its expression on social
media platforms. Specifically, we present a novel dataset of Reddit texts that
have been classified into three classes: Regret by Action, Regret by Inaction,
and No Regret. We then use this dataset to investigate the language used to
express regret on Reddit and to identify the domains of text that are most
commonly associated with regret. Our findings show that Reddit users are most
likely to express regret for past actions, particularly in the domain of
relationships. We also found that deep learning models using GloVe embedding
outperformed other models in all experiments, indicating the effectiveness of
GloVe for representing the meaning and context of words in the domain of
regret. Overall, our study provides valuable insights into the nature and
prevalence of regret on social media, as well as the potential of deep learning
and word embeddings for analyzing and understanding emotional language in
online text. These findings have implications for the development of natural
language processing algorithms and the design of social media platforms that
support emotional expression and communication.
Related papers
- Multi-class Regret Detection in Hindi Devanagari Script [1.249418440326334]
This study focuses on regret and how it is expressed, specifically in Hindi, on various social media platforms.
We present a novel dataset from three different sources, where each sentence has been manually classified into one of three classes "Regret by action", "Regret by inaction", and "No regret"
Our findings indicate that individuals on social media platforms frequently express regret for both past inactions and actions.
arXiv Detail & Related papers (2024-01-29T20:58:43Z) - Exploring Embeddings for Measuring Text Relatedness: Unveiling
Sentiments and Relationships in Online Comments [1.7230140898679147]
This paper investigates sentiment and semantic relationships among comments across various social media platforms.
It uses word embeddings to analyze components in sentences and documents.
Our analysis will enable a deeper understanding of the interconnectedness of online comments and will investigate the notion of the internet functioning as a large interconnected brain.
arXiv Detail & Related papers (2023-09-15T04:57:23Z) - 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) - REDAffectiveLM: Leveraging Affect Enriched Embedding and
Transformer-based Neural Language Model for Readers' Emotion Detection [3.6678641723285446]
We propose a novel approach for Readers' Emotion Detection from short-text documents using a deep learning model called REDAffectiveLM.
We leverage context-specific and affect enriched representations by using a transformer-based pre-trained language model in tandem with affect enriched Bi-LSTM+Attention.
arXiv Detail & Related papers (2023-01-21T19:28:25Z) - Countering Malicious Content Moderation Evasion in Online Social
Networks: Simulation and Detection of Word Camouflage [64.78260098263489]
Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems.
This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content.
arXiv Detail & Related papers (2022-12-27T16:08:49Z) - VidLanKD: Improving Language Understanding via Video-Distilled Knowledge
Transfer [76.3906723777229]
We present VidLanKD, a video-language knowledge distillation method for improving language understanding.
We train a multi-modal teacher model on a video-text dataset, and then transfer its knowledge to a student language model with a text dataset.
In our experiments, VidLanKD achieves consistent improvements over text-only language models and vokenization models.
arXiv Detail & Related papers (2021-07-06T15:41:32Z) - Sentiment analysis in tweets: an assessment study from classical to
modern text representation models [59.107260266206445]
Short texts published on Twitter have earned significant attention as a rich source of information.
Their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks.
This study fulfils an assessment of existing language models in distinguishing the sentiment expressed in tweets by using a rich collection of 22 datasets.
arXiv Detail & Related papers (2021-05-29T21:05:28Z) - Named Entity Recognition for Social Media Texts with Semantic
Augmentation [70.44281443975554]
Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts.
We propose a neural-based approach to NER for social media texts where both local (from running text) and augmented semantics are taken into account.
arXiv Detail & Related papers (2020-10-29T10:06:46Z) - "To Target or Not to Target": Identification and Analysis of Abusive
Text Using Ensemble of Classifiers [18.053219155702465]
We present an ensemble learning method to identify and analyze abusive and hateful content on social media platforms.
Our stacked ensemble comprises of three machine learning models that capture different aspects of language and provide diverse and coherent insights about inappropriate language.
arXiv Detail & Related papers (2020-06-05T06:59:22Z) - Probing Contextual Language Models for Common Ground with Visual
Representations [76.05769268286038]
We design a probing model that evaluates how effective are text-only representations in distinguishing between matching and non-matching visual representations.
Our findings show that language representations alone provide a strong signal for retrieving image patches from the correct object categories.
Visually grounded language models slightly outperform text-only language models in instance retrieval, but greatly under-perform humans.
arXiv Detail & Related papers (2020-05-01T21:28:28Z)
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.