Highlights

Abstract

Keywords

۱٫ Introduction

۲٫ Related work

۳٫ Proposed method

۴٫ Case study: user controversial behaviour in online data service

۵٫ Experiment and discussion

۶٫ Conclusion and future work

CRediT authorship contribution statement

Acknowledgments

References

Vitae

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.

۱٫ 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).