Gender Prediction for Chinese Social Media Data

Li, Wen

doi:10.26615/978-954-452-049-6_058

Cited by 12 publications

(3 citation statements)

References 15 publications

(12 reference statements)

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“…Although the high accuracy reported in the study, the methodology would not be accurate to model text data written in more formal scenarios such as customer reviews, product surveys, opinion posts, and customer service chats, which have a different structure compared to the texts that can be found in social media data. In other study [11], the authors proposed a system to classify the gender of the persons who wrote 100,000 posts from Weibo (Chinese social network similar to Tweeter). The system was based on a Word2Vec model, which achieved an accuracy of 62.9%.…”

Section: Arxiv:210702759v1 [Cscl] 23 Jun 2021mentioning

confidence: 99%

“…There are some studies focused on gender classification using Deep Learning (DL) methods. However, when considering texts in Spanish, the number of studies is relatively small [9,11]. In [10], the authors proposed a methodology based on Bidirectional Gated Recurrent Units (GRUs) and an attention mechanism for gender classification in the PAN17 corpus.…”

Section: Arxiv:210702759v1 [Cscl] 23 Jun 2021mentioning

confidence: 99%

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Gender Recognition in Informal and Formal Language Scenarios via Transfer Learning

Escobar-Grisales

Vásquez-Correa

Orozco-Arroyave

2021

Communications in Computer and Information Science

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The interest in demographic information retrieval based on text data has increased in the research community because applications have shown success in different sectors such as security, marketing, heathcare, and others. Recognition and identification of demographic traits such as gender, age, location, or personality based on text data can help to improve different marketing strategies. For instance it makes it possible to segment and to personalize offers, thus products and services are exposed to the group of greatest interest. This type of technology has been discussed widely in documents from social media. However, the methods have been poorly studied in data with a more formal structure, where there is no access to emoticons, mentions, and other linguistic phenomena that are only present in social media. This paper proposes the use of recurrent and convolutional neural networks, and a transfer learning strategy for gender recognition in documents that are written in informal and formal languages. Models are tested in two different databases consisting of Tweets and call-center conversations. Accuracies of up to 75% are achieved for both databases. The results also indicate that it is possible to transfer the knowledge from a system trained on a specific type of expressions or idioms such as those typically used in social media into a more formal type of text data, where the amount of data is more scarce and its structure is completely different.

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Section: Arxiv:210702759v1 [Cscl] 23 Jun 2021mentioning

confidence: 99%

Section: Arxiv:210702759v1 [Cscl] 23 Jun 2021mentioning

confidence: 99%

Gender Recognition in Informal and Formal Language Scenarios via Transfer Learning

Escobar-Grisales

Vásquez-Correa

Orozco-Arroyave

2021

Communications in Computer and Information Science

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“…Existing approaches utilized statistical features [115] and seldom involved background knowledge along with social information. In [116], a dataset from Sina Weibo, which is a counterpart of the micro-blogging platform Twitter, in China, was used to assess their methodology for gender prediction. [117] exploits online behavioral and textual features and choice of vocabulary for each user.…”

Section: Gender Estimation Using Twittermentioning

confidence: 99%

Predictive Analysis on Twitter: Techniques and Applications

Kursuncu

Gaur

Lokala

et al. 2018

Lecture Notes in Social Networks

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Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories.

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Using Sentiment Representation Learning to Enhance Gender Classification for User Profiling

Zheng

Zhang

et al. 2019

Web and Big Data

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User profiling means exploiting the technology of machine learning to predict attributes of users, such as demographic attributes, hobby attributes, preference attributes, etc. It's a powerful data support of precision marketing. Existing methods mainly study network behavior, personal preferences, post texts to build user profile. Through our data analysis of micro-blog, we find that females show more positive and have richer emotions than males in online social platform. This difference is very conducive to the distinction between genders. Therefore, we argue that sentiment context is important as well for user profiling.This paper focuses on exploiting microblog user posts to predict one of the demographic labels: gender. We propose a Sentiment Representation Learning based Multi-Layer Perceptron(SRL-MLP) model to classify gender. First we build a sentiment polarity classifier in advance by training Long Short-Term Memory(LSTM) model on e-commerce review corpus. Next we transfer sentiment representation to a basic MLP network. Last we conduct experiments on gender classification by sentiment representation. Experimental results show that our approach can improve gender classification accuracy by 5.53%, from 84.20% to 89.73%.

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Gender Prediction for Chinese Social Media Data

Cited by 12 publications

References 15 publications

Gender Recognition in Informal and Formal Language Scenarios via Transfer Learning

Gender Recognition in Informal and Formal Language Scenarios via Transfer Learning

Predictive Analysis on Twitter: Techniques and Applications

Using Sentiment Representation Learning to Enhance Gender Classification for User Profiling

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