Argument form and spokesperson type: The recommendation strategy of virtual salespersons

A recommendation agent (RA) at a Webstore is believed to replace the functions of communicating and facilitating purchases for which conventional salespersons are responsible in traditional stores. Seeing the RA as a virtual salesperson, this study proposed a model to demonstrate the online persuasion process between RAs and customers. Specifically, by integrating Toulmin's model of argumentation with the spokesperson strategy from marketing theory, this study developed a model illustrating that Webstore managers may be able to manipulate the argument form and spokesperson type used in RAs’ recommendations to strengthen customers’ online purchase intentions through influencing their perceived argument quality and source credibility. Based on data derived from 270 subjects who participated in a 3 × 3 laboratory experiment, findings supported five out of six hypotheses. This study has broadened the scope of RA studies by utilizing different theoretical foundations other than the trust-assuring issues that were widely discussed in the past. Findings were able to provide crucial practical implications for both scholars and Webstore managers.     

QMOS: Query-based multi-documents opinion-oriented summarization

Sentiment analysis concerns the study of opinions expressed in a text. This paper presents the QMOS method, which employs a combination of sentiment analysis and summarization approaches. It is a lexicon-based method to query-based multi-documents summarization of opinion expressed in reviews.QMOS combines multiple sentiment dictionaries to improve word coverage limit of the individual lexicon. A major problem for a dictionary-based approach is the semantic gap between the prior polarity of a word presented by a lexicon and the word polarity in a specific context. This is due to the fact that, the polarity of a word depends on the context in which it is being used. Furthermore, the type of a sentence can also affect the performance of a sentiment analysis approach. Therefore, to tackle the aforementioned challenges, QMOS integrates multiple strategies to adjust word prior sentiment orientation while also considers the type of sentence. QMOS also employs the Semantic Sentiment Approach to determine the sentiment score of a word if it is not included in a sentiment lexicon.On the other hand, the most of the existing methods fail to distinguish the meaning of a review sentence and user's query when both of them share the similar bag-of-words; hence there is often a conflict between the extracted opinionated sentences and users’ needs. However, the summarization phase of QMOS is able to avoid extracting a review sentence whose similarity with the user's query is high but whose meaning is different. The method also employs the greedy algorithm and query expansion approach to reduce redundancy and bridge the lexical gaps for similar contexts that are expressed using different wording, respectively. Our experiment shows that the QMOS method can significantly improve the performance and make QMOS comparable to other existing methods.     

Polarity shift detection,elimination and ensemble: A three-stage model for document-level sentiment analysis

The polarity shift problem is a major factor that affects classification performance of machine-learning-based sentiment analysis systems. In this paper, we propose a three-stage cascade model to address the polarity shift problem in the context of document-level sentiment classification. We first split each document into a set of subsentences and build a hybrid model that employs rules and statistical methods to detect explicit and implicit polarity shifts, respectively. Secondly, we propose a polarity shift elimination method, to remove polarity shift in negations. Finally, we train base classifiers on training subsets divided by different types of polarity shifts, and use a weighted combination of the component classifiers for sentiment classification. The results on a range of experiments illustrate that our approach significantly outperforms several alternative methods for polarity shift detection and elimination.     

Users’ ability to perceive misinformation: An information quality assessment approach

Digital information exchange enables quick creation and sharing of information and thus changes existing habits. Social media is becoming the main source of news for end-users replacing traditional media. This also enables the proliferation of fake news, which misinforms readers and is used to serve the interests of the creators. As a result, automated fake news detection systems are attracting attention. However, automatic fake news detection presents a major challenge; content evaluation is increasingly becoming the responsibility of the end-user. Thus, in the present study we used information quality (IQ) as an instrument to investigate how users can detect fake news. Specifically, we examined how users perceive fake news in the form of shorter paragraphs on individual IQ dimensions. We also investigated which user characteristics might affect fake news detection. We performed an empirical study with 1123 users, who evaluated randomly generated stories with statements of various level of correctness by individual IQ dimensions. The results reveal that IQ can be used as a tool for fake news detection. Our findings show that (1) domain knowledge has a positive impact on fake news detection; (2) education in combination with domain knowledge improves fake news detection; and (3) personality trait conscientiousness contributes significantly to fake news detection in all dimensions.     

The effects of individualism–collectivism cultural orientation on eWOM information

Drawing upon elaboration likelihood model (ELM) and espoused culture, this study predicts that eWOM reader's individualism–collectivism orientation (ICO) moderates eWOM antecedent factors’ effects on the perception of information credibility. 274 data were collected from two leading electronic word-of-mouth (eWOM) forums in China, the results confirm our prediction: eWOM reader's ICO positively moderates information sidedness’ effect on the perception of information credibility, it also negatively moderates the relationships between information consistency/information rating and information credibility. These findings validate the necessity and importance to consider readers’ espoused cultural differences in eWOM context.     

Implicit aspect extraction in sentiment analysis: Review,taxonomy, oppportunities,and open challenges

Sentiment analysis is a text classification branch, which is defined as the process of extracting sentiment terms (i.e. feature/aspect, or opinion) and determining their opinion semantic orientation. At aspect level, aspect extraction is the core task for sentiment analysis which can either be implicit or explicit aspects. The growth of sentiment analysis has resulted in the emergence of various techniques for both explicit and implicit aspect extraction. However, majority of the research attempts targeted explicit aspect extraction, which indicates that there is a lack of research on implicit aspect extraction. This research provides a review of implicit aspect/features extraction techniques from different perspectives. The first perspective is making a comparison analysis for the techniques available for implicit term extraction with a brief summary of each technique. The second perspective is classifying and comparing the performance, datasets, language used, and shortcomings of the available techniques. In this study, over 50 articles have been reviewed, however, only 45 articles on implicit aspect extraction that span from 2005 to 2016 were analyzed and discussed. Majority of the researchers on implicit aspects extraction rely heavily on unsupervised methods in their research, which makes about 64% of the 45 articles, followed by supervised methods of about 27%, and lastly semi-supervised of 9%. In addition, 25 articles conducted the research work solely on product reviews, and 5 articles conducted their research work using product reviews jointly with other types of data, which makes product review datasets the most frequently used data type compared to other types. Furthermore, research on implicit aspect features extraction has focused on English and Chinese languages compared to other languages. Finally, this review also provides recommendations for future research directions and open problems.     

GIS在税务管理中的应用

针对税务管理的现状与发展,探讨了基于GIS技术的税源监控信息系统实现的技术路线、组成和结构。将VB与Mapobjects组件相结合,开发了实用的税源监控地理信息系统。通过该系统的运行可改进现行税务工作的管理状况,提高税收工作的效率。     

新闻记者应具备的基本素质略谈

面对经济与社会转轨时期新闻传媒面对的问题,如何提高新闻记者的素质是其中一个十分重要的因素。作者通过多年实践,提出了新闻记者应具备的基本素质,即：社区大妈的亲和力、思想家的思辨能力、作家的写作能力、战士勇往直前的精神。     

QPLSA: Utilizing quad-tuples for aspect identification and rating

Aspect level sentiment analysis is important for numerous opinion mining and market analysis applications. In this paper, we study the problem of identifying and rating review aspects, which is the fundamental task in aspect level sentiment analysis. Previous review aspect analysis methods seldom consider entity or rating but only 2-tuples, i.e., head and modifier pair, e.g., in the phrase “nice room”, “room” is the head and “nice” is the modifier. To solve this problem, we novelly present a Quad-tuple Probability Latent Semantic Analysis (QPLSA), which incorporates entity and its rating together with the 2-tuples into the PLSA model. Specifically, QPLSA not only generates fine-granularity aspects, but also captures the correlations between words and ratings. We also develop two novel prediction approaches, the Quad-tuple Prediction (from the global perspective) and the Expectation Prediction (from the local perspective). For evaluation, systematic experiments show that: Quad-tuple PLSA outperforms 2-tuple PLSA significantly on both aspect identification and aspect rating prediction for publication datasets. Moreover, for aspect rating prediction, QPLSA shows significant superiority over state-of-the-art baseline methods. Besides, the Quad-tuple Prediction and the Expectation Prediction also show their strong ability in aspect rating on different datasets.     

面向社科领域的网络新闻分析与监测

通过自然语言处理技术和数理统计方法的运用,网络新闻在经济金融、公共卫生、政治科学、科研管理、舆情监测与预警等社会科学领域具有很大的利用价值。对新闻分析与监测在各个社会科学领域的应用现状进行分析和综述,包括新闻来源、关键技术、领域特点、实施方法和典型系统,总结得出当前研究的特点及发展趋势。     

Expressive signals in social media languages to improve polarity detection

Social media represents an emerging challenging sector where the natural language expressions of people can be easily reported through blogs and short text messages. This is rapidly creating unique contents of massive dimensions that need to be efficiently and effectively analyzed to create actionable knowledge for decision making processes. A key information that can be grasped from social environments relates to the polarity of text messages. To better capture the sentiment orientation of the messages, several valuable expressive forms could be taken into account. In this paper, three expressive signals – typically used in microblogs – have been explored: (1) adjectives, (2) emoticon, emphatic and onomatopoeic expressions and (3) expressive lengthening. Once a text message has been normalized to better conform social media posts to a canonical language, the considered expressive signals have been used to enrich the feature space and train several baseline and ensemble classifiers aimed at polarity classification. The experimental results show that adjectives are more discriminative and impacting than the other considered expressive signals.     

昌北高校图书馆联盟信息资源整合开源平台设计

本文首先对昌北高校图书馆联盟信息资源整合的基本模式作了简明阐述,其次对平台用到的开源技术作了简要介绍,重点对平台设计的标准、规范和流程作了详细而鲜明的论述,最后对联盟信息资源建设给出了有益建议。