AffectPT-br: an Affective Lexicon based on LIWC 2015

Carvalho, Flavio; Santos, Gabriel; Guedes, Gustavo Paiva

doi:10.1109/sccc.2018.8705251

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…The reason for this is that it is necessary to have words with semantic differentiation, including variations of gender, number, grade, etc. It is worth noting that previous studies have shown that the larger number of words in LIWC 2007pt (127, 000) may not have a positive impact on the number of words to be identified and counted in the texts, considering the problems ascertained in other studies [Carvalho et al 2018b].…”

Section: Liwc 2015ptmentioning

confidence: 84%

“…However, since the first published evaluations with LIWC 2007pt, some issues related to the performance of negative valence detection can be noticed [Balage Filho et al 2013, Rodrigues andGuedes 2017]. Recent studies are also indicating several problems with this lexicon regarding spelling mistakes and words with problems related to categorization, which negatively impacts obtained results [Carvalho et al 2018a, Carvalho et al 2018b.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Brazilian Portuguese version of the 2015 LIWC Lexicon with sentiment analysis in social networks

Carvalho

Rodrigues

Santos

et al. 2019

Anais Do Brazilian Workshop on Social Network Analysis and Mining (BraSNAM)

Self Cite

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LIWC is a text analysis program that categorizes words into gram- matical and psychologically derived categories. The currently available LIWC lexicon for Brazilian Portuguese (LIWC 2007pt) is based on the 2007 version of LIWC program. As several studies indicated, LIWC 2007pt shows perfor- mance and categorization problems. In this scenario, this work highlights a new Brazilian Portuguese LIWC lexicon (LIWC 2015pt), based on LIWC 2015 program. This work compares the performance of LIWC 2007pt and LIWC 2015pt in classification tasks. Three experiments were conducted and the results indi- cate LIWC 2015pt outperforms LIWC 2007pt in all three tasks.

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Section: Liwc 2015ptmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Evaluating the Brazilian Portuguese version of the 2015 LIWC Lexicon with sentiment analysis in social networks

Carvalho

Rodrigues

Santos

et al. 2019

Anais Do Brazilian Workshop on Social Network Analysis and Mining (BraSNAM)

Self Cite

View full text Add to dashboard Cite

show abstract

“…LIWC is used to classify narcissism [50], emotion [51], fake news [52], and propaganda [26] from the textual content. As LIWC can be used with customized dictionaries, the translated version of dictionaries are developed for linguistic analysis in French [53], Spanish [54], Chinese [55], and Portuguese [56] languages.…”

Section: A Linguistic Inquiry and Word Count (Liwc)mentioning

confidence: 99%

ProSOUL: A Framework to Identify Propaganda From Online Urdu Content

2020

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Today, the rapid dissemination of information on digital platforms has seen the emergence of information pollution such as misinformation, disinformation, fake news, and different types of propaganda. Information pollution has become a serious threat to the online digital world and has posed several challenges to social media platforms and governments around the world. In this paper, we propose Propaganda Spotting in Online Urdu Language (ProSOUL)-a framework to identify content and sources of propaganda spread in the Urdu language. First, we develop a labelled dataset of 11,574 Urdu news to train the machine learning classifiers. Next, we develop the Linguistic Inquiry and Word Count (LIWC) dictionary to extract psycho-linguistic features of Urdu text. We evaluate the performance of different classifiers by varying n-gram, News Landscape (NELA), Word2Vec, and Bidirectional Encoder Representations from Transformers (BERT) features. Our results show that the combination of NELA, word n-gram, and character n-gram features outperform with 0.91 accuracy for Urdu text classification. In addition, Word2Vec embedding outperforms BERT features in classification of the Urdu text with 0.87 accuracy. Moreover, we develop and classify large scale Urdu content repositories to identify web sources spreading propaganda. Our results show that ProSOUL framework performs best for propaganda detection in the online Urdu news content compared to the general web content. To the best of our knowledge, this is the first study on the detection of propaganda content in the Urdu language.

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“…Therefore, when dealing with special field problems, it is necessary to build a professional field dictionary. Sentiment dictionaries in different languages have been designed and applied, such as the AGUST [26] dictionary in German, Affected Br [27] in Spanish, SentiWordNet [28] in English, etc. The sentiment dictionary approach has already contributed to online education during COVID-19 [29].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Shanghai Epidemic, China, 2022 on Public Psychology: A Sentiment Analysis of Microblog Users by Data Mining

Wang

Qin

2022

Sustainability

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The Shanghai epidemic (COVID-19) is another large-scale epidemic in China’s central cities in the post-epidemic era from the end of March 2022. It is of great significance to study the public’s attitude towards the Shanghai epidemic to support healthy psychology and a positive attitude. This study used crawler technology to obtain the Weibo data related to the epidemic situation in Shanghai that was published by users. The crawled microblogs were preprocessed, and the BosonNLP sentiment dictionary for attitude classification was selected. The Chinese vocabulary ontology for 21 emotion classifications was also used. The results showed that the general attitude of the public in Shanghai was positive. This fluctuated greatly in the initial stage and gradually increased in the later stage. Through text mining, it is clear that goods and materials, nucleic acid virus testing, and other aspects of the epidemic in Shanghai are concerning for the public. The public attitude in areas that are close to the epidemic center is relatively more negative. The study can provide references for policymakers to fight COVID-19 by improving public attitude and solving urgent matters.

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AffectPT-br: an Affective Lexicon based on LIWC 2015

Cited by 9 publications

References 15 publications

Evaluating the Brazilian Portuguese version of the 2015 LIWC Lexicon with sentiment analysis in social networks

Evaluating the Brazilian Portuguese version of the 2015 LIWC Lexicon with sentiment analysis in social networks

ProSOUL: A Framework to Identify Propaganda From Online Urdu Content

The Impact of Shanghai Epidemic, China, 2022 on Public Psychology: A Sentiment Analysis of Microblog Users by Data Mining

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