Improving the Accuracy of Sentiment Analysis of SNS Comments Using Transfer Learning and Its Application to Flaming Detection

Yoshida, Shun; Kitazono, Jun; Ozawa, Seiichi; Sugawara, Takahiro; Haga, Tatsuya

doi:10.1541/ieejeiss.136.340

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…Many studies have been conducted on detecting Internet flaming (Ozawa et al, 2016;Steinberger et al, 2017;Lingam et al, 2017;Yoshida et al, 2014;Yoshida et al, 2016;Babakov et al, 2021;Ball-Burack et al, 2021). One study detected flaming based on the emotional polarity of words (Ozawa et al, 2016).…”

Section: Study Of Internet Flamingmentioning

confidence: 99%

Relationship Between Personality Patterns and Harmfulness

Matsumoto

Kishima

Tsuchiya

et al. 2022

International Journal of Information Technology and Web Engineering

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This paper hypothesize that harmful utterances need to be judged in context of whole sentences, and extract features of harmful expressions using a general-purpose language model. Based on the extracted features, we propose a method to predict the presence or absence of harmful categories. In addition, the authors believe that it is possible to analyze users who incite others by combining this method with research on analyzing the personality of the speaker from statements on social networking sites. The results confirmed that the proposed method can judge the possibility of harmful comments with higher accuracy than simple dictionary-based models or models using a distributed representations of words. The relationship between personality patterns and harmful expressions was also confirmed by an analysis based on a harmful judgment model.

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Section: Study Of Internet Flamingmentioning

confidence: 99%

Relationship Between Personality Patterns and Harmfulness

Matsumoto

Kishima

Tsuchiya

et al. 2022

International Journal of Information Technology and Web Engineering

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“…Recently, transfer learning is becoming a popular technology, since it can improve identifying performance based on data similarity (Hamidzadeh, 2015;Pan & Yang, 2010;Shao & Suzuki, 2011;Yang, Mccreadie, & Macdonald, 2017;Yin, Yu, & Sohn, 2018;Yoshida, Kitazono, Ozawa, Sugawara, & Haga, 2016). It has been used in many fields, e.g.…”

mentioning

confidence: 99%

A data transfer method for improving seed identification of maize (Zea mays) haploid breeding based on genetic similarity

Lin

et al. 2019

Plant Breeding

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Maize haploid breeding technology is able to identify haploid seeds non‐destructively, rapidly and at low cost with the help of Near‐infrared (NIR) spectral analysis. However, due to the hybridization of numerous parents and the low production rate of haploid, the haploid data collection becomes a burden for engineering this technology. Biologically, there are considerable similarities between the progeny of the same female parent and different male parents. Based on this advantage, similar spectral data can be transferred when the NIR technology is employed. A revised method of Transfer adaptive boost (TrAdaBoost) is proposed to improve identifying for the backpropagation neural network (BPNN) classifier. To avoid the negative transfer, a screening thresh is used to select out similar data, and the amount of these data are limited to join current training. The results show that the identification performances are improved significantly when the data amount is small. This method shows a high ability to make the seed identification more convenient for engineering maize haploid breeding.

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