Hair, machines, sanitary pads and diary: The sentimental intimacy of truth during the COVID-19 pandemic

The COVID-19 pandemic has been unfolding in the age of post-truth. Named as 2016’s word of the year by the Oxford Dictionary, post-truth is defined as “relating to or denoting circumstances in which objective facts are less influential in shaping public opinion than appeals to emotion and personal belief” (Mcintyre 2018, p. 5). This article considers the enduring – if not intensified – feeling of and for truth in the face of the uncertainties and competing narratives of the pandemic. As I will show, this feeling of and for truth takes a specific affective expression, which I call sentimental intimacy. Zooming in on four case studies in the Chinese context – cutting or shaving off female nurses’ hair, machines and workers at hospital construction sites, the shortage of sanitary pads, and the controversy over Fang Fang’s diary – I argue that the sentimental intimacy of truth concerns an irreducible attachment to an imagined inside whose close/d-ness undergoes constant negotiation.Peer reviewe

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Personal customized recommendation system reflecting purchase criteria and product reviews sentiment analysis

As the size of the e-commerce market grows, the consequences of it are appearing throughout society. The business environment of a company changes from a product center to a user center and introduces a recommendation system. However, the existing research has shown a limitation in deriving customized recommendation information to reflect the detailed information that users consider when purchasing a product. Therefore, the proposed system reflects the user's subjective purchasing criteria in the recommendation algorithm. And conduct sentiment analysis of product review data. Finally, the final sentiment score is weighted according to the purchase criteria priority, recommends the results to the user

Search engine For Twitter sentiment analysis

textThe purpose of sentiment analysis is to determine the attitude of a writer or a speaker with respect to some topic or his feeling in a document. Thanks to the rise of social media, nowadays there are numerous data generated by users. Mining and categorizing these data will not only bring profits for companies, but also benefit the nation. Sentiment analysis not only enables business decision makers to better understand customers' behaviors, but also allows customers to know how the public feel about a product before purchasing. On the other hand, the aggregation of emotions will effectively measure the public response toward an event or news. For example, the level of distress and sadness will increase significantly after terror attacks or natural disaster. In our project, we are going to build a search engine that allows users to check the sentiment of his query. Some of previous researches on classifying sentiment of messages on micro-blogging services like Twitter have tried to solve this problem but they have ignored neutral tweets, which will result in problematic results (12). Our sentiment analysis will also be based on tweets collected from twitter, since twitter can offer sufficient and real-time corpora for analysis. We will preprocess each tweet in the training set and label it as positive, negative or neutral. As we use words in the tweet as the feature for our model, different features will be used. We will show that accuracy achieved by different machine learning algorithms (Naïve Bayes, Maximum Entropy) can be improved with a feature vector obtained by using bigrams (5). In our practice, we find that Naive Bayes has better performance than Maximum Entropy.Statistic

From the mountains to the prairies and beyond the pale : American yodeling on early recordings

This sound review surveys yodeling in North American popular music, beginning with some of the earliest recordings on which it is featured. In order to better contextualize the recordings, I will also mention a few examples of sheet music with yodeling—items which are generally overlooked. My intention is to question why yodeling became attached to particular genres and how it functions in the construction of those genres. Indeed, two popular music genres—so-called hillbilly music and cowboy or Western music—made yodeling an important, if not identifying, component. The focus here is on yodeling’s connotations and associations and how these established expressive relationships between the moods, personae, and images of the songs

Personalized Recommendation Model: An Online Comment Sentiment Based Analysis

Traditional recommendation algorithms measure users’ online ratings of goods and services but ignore the information contained in written reviews, resulting in lowered personalized recommendation accuracy. Users’ reviews express opinions and reflect implicit preferences and emotions towards the features of products or services. This paper proposes a model for the fine-grained analysis of emotions expressed in users’ online written reviews, using film reviews on the Chinese social networking site Douban.com as an example. The model extracts feature-sentiment word pairs in user reviews according to four syntactic dependencies, examines film features, and scores the sentiment values of film features according to user preferences. User group personalized recommendations are realized through user clustering and user similarity calculation. Experiments show that the extraction of user feature-sentiment word pairs based on four syntactic dependencies can better identify the implicit preferences of users, apply them to recommendations and thereby increase recommendation accuracy

Mining Comparison Opinions from Chinese Online Reviews for Restaurant Competitive Analysis

Comparison is widely used by consumers during the process of product evaluation in order to emphasize their preference, which can contribute to a proxy for product competitiveness analysis. This paper proposes a novel method for mining comparative sentences based on the achievements of linguistic study. The definition of comparative sentence subcategory is put forward and a mixed rule pool containing both artificial rules and CSR is set up. Besides, an entity dictionary is used to re-check the identification result which can ensure precise identification and classification of comparative sentences. Real online comments are collected from Dianping.com as experimental data. The result shows that the proposed method outperforms baseline methods in terms of identification precision. Based on the result, features and opinions of comparative sentences are mined. We then conducted sentiment analysis to calculate the sentimental score of comparison relations. Finally, a feature competitive network of restaurants is constructed