مقاله انگلیسی تحلیل احساسات دهان به دهان الکترونیکی برای مطالعات رفتار کاربر
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مقاله انگلیسی تحلیل احساسات دهان به دهان الکترونیکی برای مطالعات رفتار کاربر

عنوان فارسی مقاله: تجزیه و تحلیل احساسات دهان به دهان الکترونیکی برای مطالعات رفتار کاربر
عنوان انگلیسی مقاله: E-word of mouth sentiment analysis for user behavior studies
مجله/کنفرانس: پردازش و مدیریت اطلاعات - Information Processing and Management
رشته های تحصیلی مرتبط: مدیریت
گرایش های تحصیلی مرتبط: بازاریابی، مدیریت کسب و کار، مدیریت بازرگانی، تجارت الکترونیک
کلمات کلیدی فارسی: تحلیل احساسات، تبلیغات شفاهی الکترونیکی، سرویس دوستیابی آنلاین، یادگیری ماشین
کلمات کلیدی انگلیسی: sentiment analysis - electronic word-of-mouth - online dating service - machine learning
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.ipm.2021.102784
دانشگاه: College of Computer, Jiangsu Ocean University, Lianyungang, China
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2022
ایمپکت فاکتور: 8.009 در سال 2020
شاخص H_index: 101 در سال 2021
شاخص SJR: 1.061 در سال 2020
شناسه ISSN: 0306-4573
شاخص Quartile (چارک): Q1 در سال 2020
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 12
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: دارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
آیا این مقاله فرضیه دارد: ندارد
کد محصول: E15936
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Highlights


Abstract


Keywords


1. Introduction


2. Related work


3. Proposed method


4. Case study: user controversial behaviour in online data service


5. Experiment and discussion


6. Conclusion and future work


CRediT authorship contribution statement


Acknowledgments


References


Vitae

بخشی از مقاله (انگلیسی)

Abstract


Nowadays, online word-of-mouth has an increasing impact on people's views and decisions, which has attracted many people's attention.The classification and sentiment analyse in online consumer reviews have attracted significant research concerns. In this thesis, we propose and implement a new method to study the extraction and classification of online dating services(ODS)’s comments. Different from traditional emotional analysis which mainly focuses on product attribution, we attempted to infer and extract the emotion concept of each emotional reviews by introducing social cognitive theory. In this study, we selected 4,300 comments with extremely negative/positive emotions published on dating websites as a sample, and used three machine learning algorithms to analyze emotions. When testing and comparing the efficiency of user's behavior research, we use various sentiment analysis, machine learning techniques and dictionary-based sentiment analysis. We found that the combination of machine learning and lexicon-based method can achieve higher accuracy than any type of sentiment analysis. This research will provide a new perspective for the task of user behavior.


1. Introduction


As global Internet presentations continue to increase, the number of consumers who provide online comments have increased significantly (Lu & Bai, 2021). If exploited properly, abundant data should produce useful insights. One insight that can be obtained from the statistics is the information of electronic word of mouth (EWOM). EWOM is known for its significant impact on consumer behavior (Tobon & García-Madariaga, 2021). EWOM communication framework demostrates the direct relation of adopting EWOM and consumers’ willingness to purchase. EWOM can provide objective information for more and more consumers who trust these communications (Yaniv & Shalom, 2021).Comment mining concerning sentiment analysis is considered to be a suite of proceedings for identifying sentiments, opinions and author's attitudes in texts, transforming them into meaningful information and using them to make business decisions (Siddiqui et al., 2021).


Sentiment classification identifies opinions and arguments in a given text, and it is part of opinion mining. It tries to find statements of agreement or disagreement in comments or reviews that involve positive, negative or neutral statements. Sentiment analysis has attracted widespread attention and has been widely used in many fields (Wang & Zhang, 2020). Up to now, many approaches of sentiment analysis have been proposed, which can be roughly split into document-level, sentence-level and aspect-level(Jiang, Chan, Eichelberger, Ma, & Pikkemaat, 2021).Most of the work of sentiment analysis can be achieved by assessing the document's polarity.Phrase and sentence levels have become common increasingly in recent years.Dictionary-based and machine learning approaches are two of the most common uses of emotion analysis(Ahlem & Khalil, 2020).

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