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With the speedy development of e-commerce, a growing number of customers tend to share their subjective perceptions of the product or service on the Internet. This phenomenon makes the commercial value of online reviews increasingly prominent. In this context, how to gain insights into consumers' perceptions and attitudes from massive comments has become a hot button topic. Addressing this requirement, this paper developed a fusion sentiment analysis method combining textual analysis techniques with machine learning algorithms, aiming to mine online product experience. The method mainly consists of three steps. Firstly, inspired by the sensitivity of sentiment dictionary to emotional information, we utilize the dictionary to extract sentiment features. Afterward, the SVM algorithm is adopted to identify sentiment polarities of reviews. Based on this, sentiment topics are extracted from reviews through the LDA model. Furthermore, to avoid the omission of emotional information, the dictionary is extended based on semantic similarity. Meanwhile, in this research, the fact that words in reviews have unequal sentiment contribution, which has been neglected in existing studies, is taken into account. Specifically, we introduce the weighting method to measure the sentiment contribution. Finally, the investigation of consumers' reading experiences of online books on Amazon has verified the feasibility and validity of the method. The results demonstrate that the method accurately determines reviews' emotional tendencies and captures elements affecting reading experiences from reviews. Overall, the research provides an effective way to mine online product experience and track customers' demands, thereby strongly supporting future product improvement and marketing strategy optimization. Machine learning is an important component of the growing field of data science. Through the use of statistical methods, different type of algorithms is trained to make classifications or predictions, and to uncover key insights in this project. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. Machine learning algorithms build a model based on this project data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of datasets, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.
In recent years, e-commerce has flourished and penetrated into almost all areas of our lives, particularly in consumption behavior. Various e-commerce products with high qualities and low prices have been attracting an increasing number of consumers. However, online shoppers are inclined to know more about the product through online comments, for it is not practical for them to get direct experience with the product. In this context, purchasing decisions are often strongly influenced by online reviews [15]. People would prefer to trust word-of-mouth reviews of products from other consumers rather than trust marketing advertisements, as the other consumers are considered as disinterested parties [16]. This phenomenon makes the commercial value of online reviews increasingly prominent. In fact, the research on excavating the value of massive online reviews has drawn extensive attention from the academic community. At present, many researchers have reached a general consensus that sentiment analysis is an efficacious approach to exploring the commercial value of online reviews [17], [18], [19], [20]. Sentiment analysis has been applied to many fields of contemporary organizational business activities, including enterprise management, business decision-making, etc. In terms of the value derived from the application of sentiment analysis in contemporary corporations, it is worth noting the research of Saura et al. [21] who explored major opportunities and obstacles for remote work during the COVID-19 pandemic. As they reported, their study attempted to answer ``what are the main opportunities and challenges of remote work'' by extracting relevant emotional topics from Twitter's GUC data. Obviously, their research has active guiding significance for helping companies cope with the challenges brought by the epidemic. As for business decision-making, Kauffmann et al. [22] discussed the positive role of business decisions in creating sustainable competitive advantage. He et al. [23] presented a framework to demonstrate how to make use of media data to obtain valuable knowledge for business decisions. Additionally, in aspects of products and services, as highlighted by Gonçalves et al. [24], the value of sentiment analysis lies in monitoring the reputation of products or services through review analysis, providing analytical perspectives for responding to market opinions. For instance,Wang et al. [25] explored the demanding themes and feature extension words of distinct car models by means of extracting the demand preference topic of new energy automobile customers. Their study aims to help consumers understand other users' feedback, and to facilitate car enterprises objectively capturing consumers' practical demands. Similarly, Arora et al. [26] carried out a comprehensive survey into useful insights from online platforms regarding the performance of welcome smart phone brands, battery life as well as screen quality. The survey results indicated the great latent capacity of sentiment analysis of online reviews in assessing consumers' reflections on popularized brands of the portable device. Correspondingly, Almjawel et al. [27] tried to conduct comprehensible sentiment analysis of online book reviews collected from Amazon to assist users in making efficient purchasing decisions. With respect to services, Chang et al. [28] explored the service quality of the hotel from the standpoint of consumers. By analyzing the attitudes and emotions conveyed in customer comments, they found that professional services and clean rooms have a positive effect on improving customer satisfaction. This is consistent with Freitas's emphasis on evaluating service quality from the perspective of customers [29]. Disadvantages ? An existing methodology doesn’t implement Support Vector Machines (SVM) method. ? The system not implemented the VADER-based,BosonNLP-based, KNN-based, SVM-based and BERT-based techniques.
Methodologically, we combine textual analysis techniques with machine learning algorithms. Specifically, the method mainly consists of the following steps. Firstly, the sentiment dictionary is used to extract sentiment features, based on which, the Support Vector Machines algorithm is adopted to identify sentiment polarities of review texts. Subsequently, sentiment topics are extracted from reviews with distinct sentiment polarities respectively through the Latent Dirichlet Allocation (LDA) model. The contribution of our method mainly lies in taking full advantages of the sensitivity of sentiment dictionary to emotional information and the strong generalization of machine learning algorithms. Importantly, the method overcomes demerits that the susceptibility to human interference in feature extraction of machine learning based methods and the weak adaptability in cross-domain application of dictionary-based methods. In the meantime, the dictionary is extended based on semantic similarity to avoid the omission of emotional information. Apart from that, in the present research, the fact that words in reviews have unequal sentiment contribution, which has been neglected in the existing studies, is sufficiently taken into account. The weighting method is introduced in the process of sentiment feature extraction, that is, the weight is used to measure sentiment contribution. Advantages ? In the proposed system,sentiment classification is to automatically identify sentiment polarities of review texts, thereby investigating the consumer experience of online commodities in terms of the ``positive'' and ``negative'' respectively. ? In the proposed system of SVM principles, firstly, the text vector is mapped into a space, where a maximum interval hyperplane is established. Then, the hyperplane is separated to maximize the distance between the two parallel hyperplanes.