Application of text mining techniques to the analysis of discourse in eWOM communications from a gender perspective

Teso, Elena; Olmedilla, M.; Torres, María del Rocío Martínez; Toral, S. L.

doi:10.1016/j.techfore.2017.12.018

Cited by 46 publications

(30 citation statements)

References 79 publications

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“…It has to be underlined that only recently automatic text-mining studies started investigating gender gap in online discourses (Teso et al, 2018;Chavatzia, 2017). However, these approaches mainly focused on detecting differences in language use among different genders.…”

Section: Literature Review On Relevant Past Approachesmentioning

confidence: 99%

“…The resulting network structure is informative of the cognitive layout of conceptual associations emerging from a given textual corpus and hence represents how text authors organised, structured and associated microscopically their knowledge around topics and concepts. This makes TFMNs ''glass boxes'' (Nasar, Jaffry & Malik, 2019), where the knowledge structure of a certain stance can be accessed and directly read, differently from previous ''black box'' machine learning approaches which accurately reproduced the positive or negative nature of a stance without providing information on its semantic content (Mohammad, 2016;Teso et al, 2018;Rudkowsky et al, 2018;Nasar, Jaffry & Malik, 2019). In addition to conceptual associations, TFMNs are endowed also with sentiment labels, indicating the sentiment (Warriner, Kuperman & Brysbaert, 2013) and the basic emotions (Ekman & Davidson, 1994;Mohammad & Turney, 2013) elicited by a given concept in a population of individuals involved in behavioural studies.…”

Section: Literature Review On Relevant Past Approachesmentioning

confidence: 99%

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Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media

Stella¹

2020

PeerJ Computer Science

103

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Mindset reconstruction maps how individuals structure and perceive knowledge, a map unfolded here by investigating language and its cognitive reflection in the human mind, i.e., the mental lexicon. Textual forma mentis networks (TFMN) are glass boxes introduced for extracting and understanding mindsets’ structure (in Latin forma mentis) from textual data. Combining network science, psycholinguistics and Big Data, TFMNs successfully identified relevant concepts in benchmark texts, without supervision. Once validated, TFMNs were applied to the case study of distorted mindsets about the gender gap in science. Focusing on social media, this work analysed 10,000 tweets mostly representing individuals’ opinions at the beginning of posts. “Gender” and “gap” elicited a mostly positive, trustful and joyous perception, with semantic associates that: celebrated successful female scientists, related gender gap to wage differences, and hoped for a future resolution. The perception of “woman” highlighted jargon of sexual harassment and stereotype threat (a form of implicit cognitive bias) about women in science “sacrificing personal skills for success”. The semantic frame of “man” highlighted awareness of the myth of male superiority in science. No anger was detected around “person”, suggesting that tweets got less tense around genderless terms. No stereotypical perception of “scientist” was identified online, differently from real-world surveys. This analysis thus identified that Twitter discourse mostly starting conversations promoted a majorly stereotype-free, positive/trustful perception of gender disparity, aimed at closing the gap. Hence, future monitoring against discriminating language should focus on other parts of conversations like users’ replies. TFMNs enable new ways for monitoring collective online mindsets, offering data-informed ground for policy making.

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Section: Literature Review On Relevant Past Approachesmentioning

confidence: 99%

Section: Literature Review On Relevant Past Approachesmentioning

confidence: 99%

Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media

Stella¹

2020

PeerJ Computer Science

103

View full text Add to dashboard Cite

show abstract

“…where the knowledge structure of a certain stance can be accessed and directly read, differently from previous "black box" machine learning approaches which accurately reproduced the positive or negative nature of a stance without providing information on its semantic content (Mohammad, 2016;Teso et al, 2018;Rudkowsky et al, 2018;Nasar et al, 2019). In addition to conceptual associations, TFMNs are endowed also with sentiment labels, indicating the sentiment (Warriner et al, 2013) and the basic emotions (Ekman and Davidson, 1994;Mohammad and Turney, 2013) elicited by a given concept in a…”

Section: Literature Review On Relevant Past Approachesmentioning

confidence: 99%

Peer Review #1 of "Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media (v0.2)"

2020

View full text Add to dashboard Cite

Mindset reconstruction maps how individuals structure and perceive knowledge, a map unfolded here by investigating language and its cognitive reflection in the human mind, i.e. the mental lexicon. Textual forma mentis networks (TFMN) are glass boxes introduced for extracting and understanding mindsets' structure (in Latin forma mentis) from textual data. Combining network science, psycholinguistics and Big Data, TFMNs successfully identified relevant concepts in benchmark texts, without supervision. Once validated, TFMNs were applied to the case study of distorted mindsets about the gender gap in science. Focusing on social media, this work analysed 10,000 tweets mostly representing individuals' opinions at the beginning of posts. "Gender" and "gap" elicited a mostly positive, trustful and joyous perception, with semantic associates that: celebrated successful female scientists, related gender gap to wage differences, and hoped for a future resolution. The perception of "woman" highlighted jargon of sexual harassment and stereotype threat (a form of implicit cognitive bias) about women in science "sacrificing personal skills for success". The semantic frame of "man" highlighted awareness of the myth of male superiority in science. No anger was detected around "person", suggesting that tweets got less tense around genderless terms. No stereotypical perception of "scientist" was identified online, differently from real-world surveys. This analysis thus identified the Twitter discourse mostly starting conversations as promoting a majorly stereotype-free, positive/trustful perception of gender disparity, aimed at closing the gap. Hence, future monitoring against discriminating language should focus on other parts of conversations like users' replies. TFMNs enable new ways for monitoring collective online mindsets, offering data-informed ground for policy making.

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“…Similar works [15] were also done, which considered matching user accounts on different social networks to build user profiles by user identification based on User Generated Content (UGC) in a supervised manner. These methods are all based on this assumption that the data for the same attribute or the same person has common features, such as commonality of the same gender [13], to resolve the problem of the limited labeled data. In supervised learning, current methods for tourist profiles are usually around gender, age, and other explicit feature predictions.…”

Section: Tourist Profilementioning

confidence: 99%

“…How to recognize and respond to visitors' behaviors and needs quickly and identify potential customers have become essential factors for the success of tourism stakeholders. By exploiting the subjective information contained in tourism text data, we can assist tourism stakeholders to provide better services for tourists.A large number of text mining techniques have been proposed and applied to tourism text data analysis for creating tourist profiles [8][9][10][11][12][13][14][15] and making effective market supervision [16][17][18][19][20][21][22][23][24][25]. These approaches exploit a variety of text representation strategies [26][27][28][29][30][31][32] and use different NLP techniques for topic extraction [33], text classification [34], sentiment analysis [35], and text clustering [36].…”

mentioning

confidence: 99%

A Review of Text Corpus-Based Tourism Big Data Mining

Zhang

et al. 2019

Applied Sciences

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With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists' opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years. Appl. Sci. 2019, 9, 3300 2 of 27 the travel. In order to utilize this user-generated content properly and further to meet the needs of tourists and promote the tourism industry, we need to analyze and exploit tourists' needs and opinions, and then identify the problems of tourism services or destinations, which has become a new path for tourism development. Besides, as tourism needs become increasingly personalized, visitors begin to pursue self-likeness, self-worth, and diversified travel experiences, and they are no longer willing to endure delays or waits. How to recognize and respond to visitors' behaviors and needs quickly and identify potential customers have become essential factors for the success of tourism stakeholders. By exploiting the subjective information contained in tourism text data, we can assist tourism stakeholders to provide better services for tourists.A large number of text mining techniques have been proposed and applied to tourism text data analysis for creating tourist profiles [8][9][10][11][12][13][14][15] and making effective market supervision [16][17][18][19][20][21][22][23][24][25]. These approaches exploit a variety of text representation strategies [26][27][28][29][30][31][32] and use different NLP techniques for topic extraction [33], text classification [34], sentiment analysis [35], and text clustering [36]. Moreover, while aiming to make computers understand human language, NLP has become the essential tool for text data analysis and is undergoing fast-pace growing based on the applications of deep learning in word embedding, syntax analysis, machine translation, and text understanding. Machine learning-based NLP techniques have been widely used in tourism text analysis, with superior results [19,25]. In addition, due to its high capability for extracting selective and invariant features from texts, and its independency of prior knowledge and linguistic resources, deep learning has been reported to achieve higher performance than other approaches on many NL...

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Application of text mining techniques to the analysis of discourse in eWOM communications from a gender perspective

Cited by 46 publications

References 79 publications

Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media

Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media

Peer Review #1 of "Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media (v0.2)"

A Review of Text Corpus-Based Tourism Big Data Mining

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