Multi-Level Sentiment Analysis of Product Reviews Based on Grammar Rules

Nguyen, Hien D.; Le, Thanh Van; Tran, Khiem Vinh; Luu, Son T.; Hoang, Suong N.; Phan, Hieu T.

doi:10.3233/faia210043

Cited by 5 publications

(2 citation statements)

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“…Section 3.2 presented the process for analyzing the sentiment of posts of the ADVO system (Figure 4). This process is worked to enhance the self-attention network by combining with squeeze and excitation layers [34,39]. The distribution of positive posts is a normal, binomial distribution [40], and the binomial proportion confidence interval can be determined based on the Wilson score interval method.…”

Section: Passion Pointmentioning

confidence: 99%

ADVO: A System to Manage Influencer Marketing Campaigns on Social Networks

et al. 2021

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In the fourth technology revolution, influencer marketing is an essential kind of digital marketing. This marketing uses identified influencers to viral the information of products to target customers. It is useful to support brands exposed to more valuable online consumers. The influencer marketing campaign needs a management system to manage on a social network. This system helps to increase the efficiency of a campaign. This paper proposes a management system for the influencer marketing campaign, called the ADVO system. This system provides a tool for collecting data on a social network and detecting potential brand influencers for the marketing campaign. The meaningful measures for users include amplification factors for evaluating the information propagation, the passion point to measure a user’s favorite when it comes to a brand, and the content creation score for determining the ability of post-content creating. The ADVO system helps the brand to make the decision through real-time visual reports of the campaign. It is a foundation to create commercial activities and construct an advocate community of the related brand.

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Section: Passion Pointmentioning

confidence: 99%

ADVO: A System to Manage Influencer Marketing Campaigns on Social Networks

et al. 2021

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“…Moreover, we can survey two well-known Vietnamese word segmenters, namely pyvi 1 and underthesea 2 scientifically unpublished up to now. For instances, the pyvi toolkit was used in the research of Van Thin et al [37] about sentiment analysis on VLSP2018-SA corpus [24] and research of Nguyen et al [38] on product reviews. Another instance, Nguyen et al [39] used underthesea toolkit for preprocessing their electronic products comments dataset.…”

Section: Introductionmentioning

confidence: 99%

Is word segmentation necessary for Vietnamese sentiment classification?

Nguyen¹,

Nguyen²

2022

2022 RIVF International Conference on Computing and Communication Technologies (RIVF)

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To the best of our knowledge, this paper made the first attempt to answer whether word segmentation is necessary for Vietnamese sentiment classification. To do this, we presented five pre-trained monolingual S4-based language models for Vietnamese, including one model without word segmentation, and four models using RDRsegmenter, uitnlp, pyvi, or underthesea toolkits in the pre-processing data phase. According to comprehensive experimental results on two corpora, including the VLSP2016-SA corpus of technical article reviews from the news and social media and the UIT-VSFC corpus of the educational survey, we have two suggestions. Firstly, using traditional classifiers like Naive Bayes or Support Vector Machines, word segmentation maybe not be necessary for the Vietnamese sentiment classification corpus, which comes from the social domain. Secondly, word segmentation is necessary for Vietnamese sentiment classification when word segmentation is used before using the BPE method and feeding into the deep learning model. In this way, the RDRsegmenter is the stable toolkit for word segmentation among the uitnlp, pyvi, and underthesea toolkits.

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