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Brand Potential User Identification Algorithm Based on Sentiment Analysis

This paper firstly compares the current research status of text sentiment analysis and potential customer identification, and introduces the process of building sentiment dictionaries and feature selection, feature screening, and common classification algorithms in text analysis. Secondly, around th...

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Autor principal: Li, Hongxia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9190776/
https://www.ncbi.nlm.nih.gov/pubmed/35707656
http://dx.doi.org/10.3389/fpsyg.2022.906928
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author Li, Hongxia
author_facet Li, Hongxia
author_sort Li, Hongxia
collection PubMed
description This paper firstly compares the current research status of text sentiment analysis and potential customer identification, and introduces the process of building sentiment dictionaries and feature selection, feature screening, and common classification algorithms in text analysis. Secondly, around the most used tool for sentiment analysis, sentiment dictionary, the sentiment polarity discriminative rules of sentiment words are studied. In response to the shortcomings of using a single recognition algorithm in the current process of building sentiment dictionaries, an improved integration rule is designed and an automatic construction method for domain sentiment dictionaries in the social media environment is proposed. Then, this paper analyzes the sentiment topic information existing in user-generated content and adds the domain sentiment lexicon to the joint sentiment topic model as a posteriori information to extract the sentiment topic features, based on which the feature engineering study of potential customer identification is conducted and the feature set is constructed. In addition, a sample resampling method and a diverse integration framework for unbalanced data are designed to work together for the prospect identification task under data skewing in response to the category imbalance in real data. Finally, an experimental study is conducted using a social media text corpus to validate the proposed method in this paper. The proposed domain sentiment lexicon construction method and the joint domain sentiment topic-based lead identification method show good performance in different control group experiments. This paper provides an in-depth study on the construction of domain sentiment lexicon and imbalance classification in theory and provides solutions for companies to discover potential customers in practice, which has certain theoretical significance and practical value.
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spelling pubmed-91907762022-06-14 Brand Potential User Identification Algorithm Based on Sentiment Analysis Li, Hongxia Front Psychol Psychology This paper firstly compares the current research status of text sentiment analysis and potential customer identification, and introduces the process of building sentiment dictionaries and feature selection, feature screening, and common classification algorithms in text analysis. Secondly, around the most used tool for sentiment analysis, sentiment dictionary, the sentiment polarity discriminative rules of sentiment words are studied. In response to the shortcomings of using a single recognition algorithm in the current process of building sentiment dictionaries, an improved integration rule is designed and an automatic construction method for domain sentiment dictionaries in the social media environment is proposed. Then, this paper analyzes the sentiment topic information existing in user-generated content and adds the domain sentiment lexicon to the joint sentiment topic model as a posteriori information to extract the sentiment topic features, based on which the feature engineering study of potential customer identification is conducted and the feature set is constructed. In addition, a sample resampling method and a diverse integration framework for unbalanced data are designed to work together for the prospect identification task under data skewing in response to the category imbalance in real data. Finally, an experimental study is conducted using a social media text corpus to validate the proposed method in this paper. The proposed domain sentiment lexicon construction method and the joint domain sentiment topic-based lead identification method show good performance in different control group experiments. This paper provides an in-depth study on the construction of domain sentiment lexicon and imbalance classification in theory and provides solutions for companies to discover potential customers in practice, which has certain theoretical significance and practical value. Frontiers Media S.A. 2022-05-30 /pmc/articles/PMC9190776/ /pubmed/35707656 http://dx.doi.org/10.3389/fpsyg.2022.906928 Text en Copyright © 2022 Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Li, Hongxia
Brand Potential User Identification Algorithm Based on Sentiment Analysis
title Brand Potential User Identification Algorithm Based on Sentiment Analysis
title_full Brand Potential User Identification Algorithm Based on Sentiment Analysis
title_fullStr Brand Potential User Identification Algorithm Based on Sentiment Analysis
title_full_unstemmed Brand Potential User Identification Algorithm Based on Sentiment Analysis
title_short Brand Potential User Identification Algorithm Based on Sentiment Analysis
title_sort brand potential user identification algorithm based on sentiment analysis
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9190776/
https://www.ncbi.nlm.nih.gov/pubmed/35707656
http://dx.doi.org/10.3389/fpsyg.2022.906928
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