Affect as a proxy for literary mood

04/06/2023
by   Emily Öhman, et al.
0

We propose to use affect as a proxy for mood in literary texts. In this study, we explore the differences in computationally detecting tone versus detecting mood. Methodologically we utilize affective word embeddings to look at the affective distribution in different text segments. We also present a simple yet efficient and effective method of enhancing emotion lexicons to take both semantic shift and the domain of the text into account producing real-world congruent results closely matching both contemporary and modern qualitative analyses.

READ FULL TEXT
research
03/30/2022

Asymmetric Proxy Loss for Multi-View Acoustic Word Embeddings

Acoustic word embeddings (AWEs) are discriminative representations of sp...
research
03/18/2015

Text Segmentation based on Semantic Word Embeddings

We explore the use of semantic word embeddings in text segmentation algo...
research
04/03/2019

Black is to Criminal as Caucasian is to Police:Detecting and Removing Multiclass Bias in Word Embeddings

Online texts -- across genres, registers, domains, and styles -- are rid...
research
06/05/2019

Entity-Centric Contextual Affective Analysis

While contextualized word representations have improved state-of-the-art...
research
09/14/2023

Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated Text

While recent advancements in the capabilities and widespread accessibili...
research
10/07/2020

MuSeM: Detecting Incongruent News Headlines using Mutual Attentive Semantic Matching

Measuring the congruence between two texts has several useful applicatio...
research
05/13/2020

Sanskrit Segmentation Revisited

Computationally analyzing Sanskrit texts requires proper segmentation in...

Please sign up or login with your details

Forgot password? Click here to reset