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This paper will discuss the integration of document image processing and text retrieval principles in order to process and load existing paper documents automatically in an electronic document database that broadens the user's capability to retrieve relevant information more accurately, without going through costly processes to get paper documents into electronic text. The principles of document image processing systems, as well as the problems and shortcomings of most of today's document image processing systems, will be discussed. Then concept retrieval as the latest development in text retrieval will be discussed, with specific reference to the ability of the TOPIC intelligent text retrieval system to allow users to build up a knowledge base of search objects or concepts that can be used at any point in time by all users for the system. This paper will further specifically look at the automatic processing of paper documents by converting the scanned document image pages through to electronic text. The use of optical character recognition technology, the indexing and loading of the documents in a text database, the automatic linking of the documents to the related document images and the retrieval technology available in TOPIC, specifically the TYPO operator that was developed to handle so‐called dirty data such as the common misspellings, character transpositions and ‘dirty’ text received as output from the OCR process, will be discussed. A possible solution to load paper documents quickly and cost‐effectively into an electronic document database will be discussed and demonstrated in detail. The advantages and disadvantages of this approach will be discussed with specific reference to an electronic news clipping service application.

This article describes the fastest growing category of machine‐readable data‐bases — full‐text databases. A selection of articles from the literature on full‐text databases was explored and this provides a basis for the information presented here on search strategy, performance measurement, and benefits and limitations of full‐text databases. Various use studies and uses of full‐text databases have also been listed.

This article discusses full‐text source lists used by full‐text finding tools, such as serials management systems, OpenURL link resolvers, and imported e‐journal MARC records. Although the vendors of full‐text finding tools claim that they frequently update their full‐text source lists with changes in full‐text titles, ISSNs, coverage dates, and other information, they actually rely on content providers to offer title lists and coverage information. Not all content providers offer accurate and updated full‐text source lists in terms of full‐text titles included, coverage dates and embargo periods, and formats and file types. As a result, librarians and users using serials management systems, OpenURL link resolvers, or OPACs for finding full‐text periodicals are sometimes taken to dead ends. Vendors of both full‐text finding tools and full‐text content need to improve the accuracy and currency of their services.

The purpose of this paper is to research integrating web text resources and mine its emergence.

With the understanding of characteristics of internet resources, this paper will focus on solving the problem of text resource aggregation in open environment and its emergence showed during aggregation over time. The authors process these text resources, both in space and time dimension, through viewing them as an event stream evolving over time, and attempt to discover the evolutionary event patterns and furthermore, to mine the emergence of text content.

The proposed methods are generally applicable to text stream data and have many potential applications in text resource aggregation in open environment.

The main limitation is availability of data.

The paper presents a very useful method for text resource aggregation in an open environment.

The paper presents a new method to integrate web text resources and mine its emergence.

A recent thread in the DIG_REF listserv addressed the integration of text messaging into mainstream reference service. The purpose of this paper is to expand upon that discussion, pointing out the predominant software used by libraries to handle text message reference questions and the volume of reference traffic generated by text messaging queries.

The paper also addresses the ramifications on staffing of the added traffic in text messaging and how libraries might market text messaging reference services to their patrons.

The paper further discusses the unique nature of text messaging queries and how this affects the reference interview.

The paper is intended for the reference services manager looking to incorporate text messaging into a library's repertoire of reference services.

Providing convenient access to journals for users in a geographically dispersed university was a challenge for the Pennsylvania State University Libraries’ Full‐Text Implementation Group. The group established and implemented procedures for providing full‐text access to general interest periodicals to all Penn State users, both in the libraries and remotely. This paper discusses the formation of the group, addresses the decisions made about providing full‐text, the procedures established to implement and promote full‐text, the problems encountered during implementation, and the future of full‐text access at Penn State.

Using a discourse analytic approach, the purpose of this paper is to examine how genre impacts white readers when reading about historic acts of racial violence. Specifically, this study explores one white high school student’s stance-taking as she read an informational text and an eyewitness narrative about the Tulsa Race Massacre.

This study used discourse analysis (Gee, 1999) and the think-aloud method (Pressley and Afflerbach, 1996) to explore the white student’s interactions with genres of historical texts. The authors coupled iterative coding and memoing with discourse analysis to analyze the stances she adopted while reading.

The findings illustrate that the informational text allowed for a distancing from the racialized violence in the text, whereas the narrative created an opportunity for more connection to those who experienced the violence.

While genre and reader response has long been explored in English Education research, little research has examined the impact of genre on reading historical texts. This study demonstrates the influence that genre may have on white readers’ emotional responses and stance-taking practices when reading about historic acts of racial violence.

Research is needed regarding how to influence young adults’ patterns of cell phone use while driving, amid social pressures to stay connected to their peers. Such insight could form the basis of a social marketing campaign. This study aims to explore the potential of inoculation and narrative messages as strategies to protect (i.e. generate resistance against) negative attitudes toward texting and driving.

Using a three-phase experiment, the investigation explored the impact of different communication message strategies (i.e. inoculation, narrative, control) aimed at reducing texting while driving.

Results indicated that, for college students exposed to messages in support of texting and driving, inoculation messages were superior to both narrative and control messages. These findings can guide the development of strategic social marketing interventions.

Social marketing scholars and practitioners should consider weaving inoculation messages throughout social marketing campaigns focused on this important issue.

To the authors’ knowledge, this is the first study to evaluate and compare inoculation and narrative strategies in the context of texting and driving.

The current study investigates the impact on perceived review helpfulness of the simultaneous processing of information from multiple cues with various central and peripheral cue combinations based on the elaboration likelihood model (ELM). Thus, the current study develops and tests hypotheses by analyzing real-world review data with a text mining approach in e-commerce to investigate how information consistency (rating inconsistency, review consistency and text similarity) influences perceived helpfulness. Moreover, the role of product type is examined in online consumer reviews of perceived helpfulness.

The current study collected 61,900 online reviews, including 600 products in six categories, from Amazon.com. Additionally, 51,927 reviews were filtered that received helpfulness votes, and then text mining and negative binomial regression were applied.

The current study found that rating inconsistency and text similarity negatively affect perceived helpfulness and that review consistency positively affects perceived helpfulness. Moreover, peripheral cues (rating inconsistency) positively affect perceived helpfulness in reviews of experience goods rather than search goods. However, there is a lack of evidence to demonstrate the hypothesis that product types moderate the effectiveness of central cues (review consistency and text similarity) on perceived helpfulness.

Previous studies have mainly focused on numerical and textual factors to investigate the effect on perceived helpfulness. Additionally, previous studies have independently confirmed the factors that affect perceived helpfulness. The current study investigated how information consistency affects perceived helpfulness and found that various combinations of cues significantly affect perceived helpfulness. This result contributes to the review helpfulness and ELM literature by identifying the impact on perceived helpfulness from a comprehensive perspective of consumer review and information consistency.

This study aimed to present the methodology of the text data analysis to establish marketing strategies for fintech companies in a practical way. Specifically, the methodology was presented to convert customers' review data, which consisted of the text data (unstructured data), to the numerical data (structured data) by using a text mining algorithm “Global Vectors for Word Representation,” abbreviated as “GloVe”; additionally, the authors presented the methodology to deploy the numerical data for marketing strategies with eliminate-reduce-raise-create (ERRC) value factor analytics.

First, the authors defined the background, features and contents of fintech services based on a review of related literature review. Additionally, they examined business strategies, the importance of social media for fintech services and fintech technology trends based on the literature review. Next, they analyzed the similarity between fintech-related keywords, which represent the trends in fintech services, and the text data related to fintech corporations and their services posted on Facebook and Twitter, which are two of the most popular social media globally, during the period 2017–2019. The similarity was then quantified and categorized in terms of the representative global fintech companies and the status of each fintech service sector. Furthermore, the similarity was visualized, and value elements were rebuilt using ERRC strategy analytics.

This study is meaningful in that it quantifies the degree of similarity between customers' responses, experiences and expectations regarding the rapidly growing global fintech firms' services and trends in fintech services.

This study suggests a practical way to apply in business by providing a method for transforming unstructured text data into structured numerical data it is measurable. It is expected that this study can be used as the basis for exploring sustainable development strategies for the fintech industry.