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‘Thesaurus’ comes from the Greek—a storehouse or treasury. The Shorter Oxford English dictionary gives 1736 for the English usage ‘a treasury or storehouse of knowledge, as a dictionary, encyclopaedia or the like’. In 1852, Peter Mark Roget published his Thesaurus of English words and phrases: ‘a collection … arranged, not in alphabetical order as they are in a dictionary, but according to the ideas which they express… The object aimed at is, the idea being given, to find the word, or words, by which that idea may be most fitly and aptly expressed.’ All who write English have heard of this thesaurus.

This study aims to explore the role of the linguistic style used in the brand-posted social media content on consumer engagement in the Fintech domain.

A total of 3,286 tweets (registering nearly 1.35 million impressions) published by 10 leading Fintech unicorns in India were extracted using the Twitter API. The Linguistic Inquiry and Word Count (LIWC) dictionary was used to analyse the linguistic characteristics of the shared tweets. Negative Binomial Regression (NBR) was used for testing the hypotheses.

This study finds that using drive words and cognitive language increases consumer engagement with Fintech messages via the central route of information processing. Further, affective words and conversational language drive consumer engagement through the peripheral route of information processing.

The study extends the literature on brand engagement by unveiling the effect of linguistic features used to design social media messages.

The study provides guidance to social media marketers of Fintech brands regarding what content strategies best enhance consumer engagement. The linguistic style to improve online consumer engagement (OCE) is detailed.

The study’s findings contribute to the growing stream of Fintech literature by exploring the role of linguistic style on consumer engagement in social media communication. The study’s findings indicate the relevance of the dual processing mechanism of elaboration likelihood model (ELM) as an explanatory theory for evaluating consumer engagement with messages posted by Fintech brands.

Opinion mining (OM), also known as “sentiment classification”, which aims to discover common patterns of user opinions from their textual statements automatically or semi-automatically, is not only useful for customers, but also for manufacturers. However, because of the complexity of natural language, there are still some problems, such as domain dependence of sentiment words, extraction of implicit features and others. The purpose of this paper is to propose an OM method based on topic maps to solve these problems.

Domain-specific knowledge is key to solve problems in feature-based OM. On the one hand, topic maps, as an ontology framework, are composed of topics, associations, occurrences and scopes, and can represent a class of knowledge representation schemes. On the other hand, compared with ontology, topic maps have many advantages. Thus, it is better to integrate domain-specific knowledge into OM based on topic maps. This method can make full use of the semantic relationships among feature words and sentiment words.

In feature-level OM, most of the existing research associate product features and opinions by their explicit co-occurrence, or use syntax parsing to judge the modification relationship between opinion words and product features within a review unit. They are mostly based on the structure of language units without considering domain knowledge. Only few methods based on ontology incorporate domain knowledge into feature-based OM, but they only use the “is-a” relation between concepts. Therefore, this paper proposes feature-based OM using topic maps. The experimental results revealed that this method can improve the accuracy of the OM. The findings of this study not only advance the state of OM research but also shed light on future research directions.

To demonstrate the “feature-based OM using topic maps” applications, this work implements a prototype that helps users to find their new washing machines.

This paper presents a new method of feature-based OM using topic maps, which can integrate domain-specific knowledge into feature-based OM effectively. This method can improve the accuracy of the OM greatly. The proposed method can be applied across various application domains, such as e-commerce and e-government.

In order to succeed in an action under the Equal Pay Act 1970, should the woman and the man be employed by the same employer on like work at the same time or would the woman still be covered by the Act if she were employed on like work in succession to the man? This is the question which had to be solved in Macarthys Ltd v. Smith. Unfortunately it was not. Their Lordships interpreted the relevant section in different ways and since Article 119 of the Treaty of Rome was also subject to different interpretations, the case has been referred to the European Court of Justice.

An Act to amend the law relating to employers and workers and to organisations of employers and organisations of workers; to provide for the establishment of a National Industrial Relations Court and for extending the jurisdiction of industrial tribunals; to provide for the appointment of a Chief Registrar of Trade Unions and Employers' Associations, and of assistant registrars, and for establishing a Commission on Industrial Relations as a statutory body; and for purposes connected with those matters. [5th August 1971]

Indexing documents can be a purely mechanical operation or a highly intelligent one. It can be nothing more than a matter of listing the words in the title of the document, or it can be a detailed intellectual analysis of its content. But however the work is done, it ends up in the same way: the document gets labelled with a set of words (D). When it comes to searching the index, we again have to make up a set of words (Q). Retrieval is the operation of matching question words against document words. In co‐ordinate indexing, a document is retrieved if its word labels D include the question words Q. If we alter the words used for documents or questions, we affect the efficiency of the system.

In response to questions by Buxton and Meadows, there was an examination of the occurrence of title words in the abstracts, first paragraphs, last paragraphs and cited titles of research papers in chemistry, economics, history, mathematics and philosophy for the 1960 and 1970 eras. Title word occurrence in first paragraphs varied little among disciplines. Last paragraphs tended to have most frequent occurrence of title words in history and philosophy, and cited titles had most frequent occurrence in chemistry and mathematics. There was no significant difference between chemistry and mathematics of occurrence in abstracts; abstracts were not available for the other disciplines. Among disciplines taken as a whole, the best reflection of title word occurrence was the collection of abstracts, followed in order by first paragraphs, last paragraphs and cited titles. First and last paragraphs together provided 70% to 80% of the title words. For most disciplines, longer than average titles did demonstrate a higher frequency of title word occurrence in first and last paragraphs than did titles in general. The results implied that indexing based on extraction of title words could employ similar procedures from discipline to discipline. Nevertheless, sensitive information retrieval systems should be prepared for changes in the vocabulary of fields like history and philosophy to occur possibly more slowly than in fields like mathematics and chemistry.

This paper aims to extend the understanding of the ways in which social entrepreneurs give sense to and legitimize their work by introducing a rhetoric-orientation view of social entrepreneurship (SE).

This study uses computer-aided text analysis and computational linguistics to study 191 interviews of social and business entrepreneurs. It offers validation and exploration of new concepts pertaining to the rhetoric orientations of SE.

This study confirms prior untested assumptions that the rhetoric of social entrepreneurs is more other, stakeholder engagement and justification-oriented and less self-oriented than the rhetoric of business entrepreneurs. It also confirms that the rhetoric of both types of entrepreneurs is equally economically oriented.

This research makes new contribution to the SE literature by introducing three new orientations, namely, solution, impact and geographical, which reflect distinctive rhetorical themes used by social entrepreneurs, and by revealing that social entrepreneurs use terms associated with other, stakeholder engagement, justification, economic, solution, impact and geographical orientations differently than business entrepreneurs.

The prediction of pre-election polls is an issue of concern for both politicians and voters. The Taiwan nine-in-one election held in 2014 ended with jaw-dropping results; apparently, traditional polls did not work well. As a remedy to this problem, the purpose of this paper is to utilize the comments posted on social media to analyze civilians’ views on the two candidates for mayor of Taichung City, Chih-chiang Hu, and Chia-Lung Lin.

After conducting word segmentation and part-of-speech tagging for the collected reviews, this study constructs the opinion phrase extraction rules for identifying the opinion words associated with the attribute words. Next, this study classifies the attribute words into six municipal governance-related topics and calculates the opinion scores for each candidate. Finally, this study uses correspondence analysis to transform opinion information on the candidates into a graphical display to facilitate the interpretation of voters’ views.

The results show that the topics of candidates’ backgrounds and transport infrastructure were the two most critical factors for the election prediction. Based on the predication, Lin outscores Hu by 17.74 percent which is close to the real election results.

This study proposes new rules for the extraction of Chinese opinion words associated with attribute words.

This study applies Chinese semantic analysis to assist in predicting election results and investigating the topics of concern to voters.

The proposed opinion phrase extraction rules for Chinese social media, as well as the election forecast process, can provide valuable references for political parties and candidates to plan better nomination and election strategies.

Sentiment lexicon is an essential resource for sentiment analysis of user reviews. By far, there is still a lack of domain sentiment lexicon with large scale and high accuracy for Chinese book reviews. This paper aims to construct a large-scale sentiment lexicon based on the ultrashort reviews of Chinese books.

First, large-scale ultrashort reviews of Chinese books, whose length is no more than six Chinese characters, are collected and preprocessed as candidate sentiment words. Second, non-sentiment words are filtered out through certain rules, such as part of speech rules, context rules, feature word rules and user behaviour rules. Third, the relative frequency is used to select and judge the polarity of sentiment words. Finally, the performance of the sentiment lexicon is evaluated through experiments.

This paper proposes a method of sentiment lexicon construction based on ultrashort reviews and successfully builds one for Chinese books with nearly 40,000 words based on the Douban book.

Compared with the idea of constructing a sentiment lexicon based on a small number of reviews, the proposed method can give full play to the advantages of data scale to build a corpus. Moreover, different from the computer segmentation method, this method helps to avoid the problems caused by immature segmentation technology and an imperfect N-gram language model.