Papers with LIWC
A Study on Using Semantic Word Associations to Predict the Success of a Novel (2021.starsem-1)
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Syeda Jannatus Saba, Biddut Sarker Bijoy, Henry Gorelick, Sabir Ismail, Md Saiful Islam, Mohammad Ruhul Amin
| Challenge: | Existing methods for book success prediction are not effective. |
| Approach: | They propose to represent a book as a spectrum of concepts based on the association score between its content embedding and a global embeddment for a set of semantically linked word clusters. |
| Outcome: | The proposed method outperforms the previous methods for book success prediction. |
Tracing Linguistic Markers of Influence in a Large Online Organisation (2023.acl-short)
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Prashant Khare, Ravi Shekhar, Mladen Karan, Stephen McQuistin, Colin Perkins, Ignacio Castro, Gareth Tyson, Patrick Healey, Matthew Purver
| Challenge: | Social science and psycholinguistic research have shown that power and status affect how people use language in a range of domains. |
| Approach: | They propose to use lexical categories and BERT to predict levels of influence in an online community and identify key linguistic differences between people before and after becoming influential. |
| Outcome: | The results show that participants' levels of influence can be predicted from their email text, and identify key differences in language use for the same person before and after becoming influential. |
Automatic identification of writers’ intentions: Comparing different methods for predicting relationship goals in online dating profile texts (D19-55)
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| Challenge: | lexicon-based text analysis methods such as LIWC have been criticized by computational linguists for their lack of adaptability, but they have not been systematically compared with either human evaluations or machine learning approaches. |
| Approach: | They used a corpus of online dating profile texts to compare LIWC, machine learning, and a human baseline to assess their effectiveness on a relationship goal classification task. |
| Outcome: | The proposed methods were compared with a corpus of online dating profile texts and a human baseline. |
Detection of Propaganda Using Logistic Regression (D19-50)
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| Challenge: | Various propaganda techniques are used to manipulate peoples perspectives to foster a predetermined agenda. |
| Approach: | They propose a Logistic Regression-based tool that automatically classifies whether a sentence is propagandistic or not. |
| Outcome: | The proposed tool outperforms the baseline on linguistic and semantic features. |
Linguistic Analysis of Veteran Job Interviews to Assess Effectiveness in Translating Military Expertise to the Civilian Workforce (2025.naacl-srw)
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| Challenge: | Using natural language processing, veterans are more likely to be hired than general population. |
| Approach: | They conduct NLP experiments to evaluate the degree of explanation in veteran job interview responses as a proxy for perceived hireability. |
| Outcome: | The results show that the proposed method is robust to linguistic features and features. |
Stigma Annotation Scheme and Stigmatized Language Detection in Health-Care Discussions on Social Media (2020.lrec-1)
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| Challenge: | a large amount of research has been done on the interpretation and influence of stigma on human behaviour and health. |
| Approach: | They develop an annotation scheme and improve the annotation process for stigma identification . they aim to distinguish stigmatised language from non-stigmatised using machine learning and NLP . |
| Outcome: | The proposed method improves the annotation process for stigma identification . the results show that the method performs better than other models . |
Psycholinguistic Tripartite Graph Network for Personality Detection (2021.acl-long)
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| Challenge: | Existing work on personality detection from online posts adopts multifarious deep neural networks to represent the posts and builds predictive models in a data-driven manner without the exploitation of psycholinguistic knowledge. |
| Approach: | They propose a psycholinguistic knowledge-based tripartite graph network, TrigNet, which consists of a tripartitic graph network and a BERT-based graph initializer. |
| Outcome: | The proposed graph network outperforms the existing state-of-the-art model by 3.47 and 2.10 points in average F1 on two datasets. |
Lying Through One’s Teeth: A Study on Verbal Leakage Cues (2021.emnlp-main)
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| Challenge: | Existing studies on verbal leakage cues do not address their impact on models' validity. |
| Approach: | They propose to use LIWC to show verbal leakage cues in lie detection datasets to understand their effect on data collection and examine their validity. |
| Outcome: | The proposed models with more strong verbal leakage cue categories perform better than models trained on a dataset with only a greater number of strong cues. |
Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depression (2022.lrec-1)
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| Challenge: | a new study explores the relationship between depression and manifestations of happiness in social media . we use Positive-Unlabeled learning paradigm to extract happy moments from social media posts . 264 million people of all ages suffer from depression, according to the u.s. |
| Approach: | They propose a positive-unlabeled learning paradigm to extract happy moments from social media . they use LIWC and keyness information to qualitatively analyze the happy moments . |
| Outcome: | The proposed method extracts happy moments from social media posts of depressed users and controls . it qualitatively analyzes the results with LIWC and keyness information . |
Incorporating LIWC in Neural Networks to Improve Human Trait and Behavior Analysis in Low Resource Scenarios (2022.lrec-1)
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| Challenge: | Psycholinguistic knowledge resources have been widely used in constructing features for text-based human trait and behavior analysis. |
| Approach: | They propose to incorporate a widely-used psycholinguistic lexicon into NN models to improve human trait and behavior analysis in low resource scenarios. |
| Outcome: | The proposed methods perform significantly better than baselines that use only LIWC or NN-based feature learning methods. |
UMUTextStats: A linguistic feature extraction tool for Spanish (2022.lrec-1)
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| Challenge: | Feature Engineering is the application of domain knowledge to build efficient machine learning models. |
| Approach: | a team of researchers has developed a linguistic extraction tool for Spanish . the tool uses linguistic features and embeddings to build efficient machine learning models . |
| Outcome: | UMUTextStats is a linguistic extraction tool for Spanish . it has been validated in infodemiology, hate-speech detection, author profiling, authorship verification, humour or irony detection, among others. |