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The purpose of this paper is to describe and position the state-of-the-art of text and image mining methods in business research. By providing a detailed conceptual and technical review of both methods, it aims to increase their utilization in service research.

On a first stage, the authors review business literature in marketing, operations and management concerning the use of text and image mining methods. On a second stage, the authors identify and analyze empirical papers that used text and image mining methods in services journals and premier business. Finally, avenues for further research in services are provided.

The manuscript identifies seven text mining methods and describes their approaches, processes, techniques and algorithms, involved in their implementation. Four of these methods are positioned similarly for image mining. There are 39 papers using text mining in service research, with a focus on measuring consumer sentiment, experiences, and service quality. Due to the nonexistent use of image mining service journals, the authors review their application in marketing and management, and suggest ideas for further research in services.

This manuscript focuses on the different methods and their implementation in service research, but it does not offer a complete review of business literature using text and image mining methods.

The results have a number of implications for the discipline that are presented and discussed. The authors provide research directions using text and image mining methods in service priority areas such as artificial intelligence, frontline employees, transformative consumer research and customer experience.

The manuscript provides an introduction to text and image mining methods to service researchers and practitioners interested in the analysis of unstructured data. This paper provides several suggestions concerning the use of new sources of data (e.g. customer reviews, social media images, employee reviews and emails), measurement of new constructs (beyond sentiment and valence) and the use of more recent methods (e.g. deep learning).

This paper develops a generalizable, machine-learning-based method for measuring established marketing constructs using passive analysis of consumer-generated textual data from service reviews. The method is demonstrated using topic and sentiment analysis along dimensions of an existing scale: lodging quality index (LQI).

The method induces numerical scale ratings from text-based data such as consumer reviews. This is accomplished by automatically developing a dictionary from words within a set of existing scale items, rather a more manual process. This dictionary is used to analyze textual consumer review data, inducing topic and sentiment along various dimensions. Data produced is equivalent with Likert scores.

Paired t-tests reveal that the text analysis technique the authors develop produces data that is equivalent to Likert data from the same individual. Results from the authors’ second study apply the method to real-world consumer hotel reviews.

Results demonstrate a novel means of using natural language processing in a way to complement or replace traditional survey methods. The approach the authors outline unlocks the ability to rapidly and efficiently analyze text in terms of any existing scale without the need to first manually develop a dictionary.

The technique makes a methodological contribution by outlining a new means of generating scale-equivalent data from text alone. The method has the potential to both unlock entirely new sources of data and potentially change how service satisfaction is assessed and opens the door for analysis of text in terms of a wider range of constructs.

In the Indian judicial system, the court considers interpretations of similar previous judgments for the present case. An essential requirement of legal practitioners is to determine the most relevant judgments from an enormous amount of judgments for preparing supportive, beneficial and favorable arguments against the opponent. It urges a strong demand to develop a Legal Document Recommendation System (LDRS) to automate the process. In existing works, traditionally preprocessed judgment corpus is processed by Doc2Vec to learn semantically rich judgment embedding space (i.e. vector space). Here, vectors of semantically relevant judgments are in close proximity, as Doc2Vec can effectively capture semantic meanings. The enormous amount of judgments produces a huge noisy corpus and vocabulary which possesses a significant challenge: traditional preprocessing cannot fully eliminate noisy data from the corpus and due to this, the Doc2Vec demands huge memory and time to learn the judgment embedding. It also adversely affects the recommendation performance in terms of correctness. This paper aims to develop an effective and efficient LDRS to support civilians and the legal fraternity.

To overcome previously mentioned challenges, this research proposes the LDRS that uses the proposed Generalized English and Indian Legal Dictionary (GEILD) which keeps the corpus of relevant dictionary words only and discards noisy elements. Accordingly, the proposed LDRS significantly reduces the corpus size, which can potentially improve the space and time efficiency of Doc2Vec.

The experimental results confirm that the proposed LDRS with GEILD yield superior performance in terms of accuracy, F1-Score, MCC-Score, with significant improvement in the space and time efficiency.

The proposed LDRS uses the customized domain-specific preprocessing and novel legal dictionary (i.e. GEILD) to precisely recommend the relevant judgments. The proposed LDRS can be incorporated with online legal search repositories/engines to enrich their functionality.

In computational chemistry, the chemical bond energy (pKa) is essential, but most pKa-related data are submerged in scientific papers, with only a few data that have been extracted by domain experts manually. The loss of scientific data does not contribute to in-depth and innovative scientific data analysis. To address this problem, this study aims to utilize natural language processing methods to extract pKa-related scientific data in chemical papers.

Based on the previous Bert-CRF model combined with dictionaries and rules to resolve the problem of a large number of unknown words of professional vocabulary, in this paper, the authors proposed an end-to-end Bert-CRF model with inputting constructed domain wordpiece tokens using text mining methods. The authors use standard high-frequency string extraction techniques to construct domain wordpiece tokens for specific domains. And in the subsequent deep learning work, domain features are added to the input.

The experiments show that the end-to-end Bert-CRF model could have a relatively good result and can be easily transferred to other domains because it reduces the requirements for experts by using automatic high-frequency wordpiece tokens extraction techniques to construct the domain wordpiece tokenization rules and then input domain features to the Bert model.

By decomposing lots of unknown words with domain feature-based wordpiece tokens, the authors manage to resolve the problem of a large amount of professional vocabulary and achieve a relatively ideal extraction result compared to the baseline model. The end-to-end model explores low-cost migration for entity and relation extraction in professional fields, reducing the requirements for experts.

The purpose of this paper is to provide an overview of higher education students’ experiences in using smartphones for learning purposes, and their perceptions of the suitability of smartphones for learning.

A qualitative research method is applied to data collection and analysis by following the grounded theory approach. Data were gathered by an online focus group involving Library and Information Science (LIS) students from University of Hong Kong and University of Tsukuba (Japan).

LIS students at both universities regularly use smartphones for communication, socializing, entertainment and other daily information needs. The findings show that LIS students commonly use smartphones for learning and consider smartphones to be very useful for their academic work. They use smartphones to access course materials, search library catalog, discuss course assignments with peers, take notes, etc. Although both academic libraries involved offer a variety of services for mobile devices, these services are still not used frequently. A major barrier to using smartphone for academic learning is the smartphone’s small screen.

The study relies on a convenience sample, restricted to students from two universities, one from Hong Kong and the other from Japan. Further research on a larger sample is recommended.

The study adds to the knowledge of smartphone actual use for learning purposes and provides study participants’ insights on the usefulness of smartphones for learning.

This paper characterizes translation as a task and aims to identify how it influences professional translators' information needs and use of resources to meet those needs.

This research is exploratory and qualitative. Data are based on focus group sessions with 19 professional translators. Where appropriate, findings are related to several theories relating task characteristics and information behavior (IB).

The findings support some of Byström's findings about relationship between task and information use but also suggest new hypotheses or relationships among task, information need, and information use, including the notion of a zone of familiarity. Translators use a wide range of resources, both formal and informal, localized sources, including personal contacts with other translators, native speakers, and domain experts, to supplement their basic resources, which are different types of dictionaries. The study addresses translator problems created by the need to translate materials in less commonly taught languages.

Focus group sessions allow only for identifying concepts, relationships, and hypotheses, not for indicating the relative importance of variables or distribution across individuals. Translation does not cover literary translation.

The paper suggests content and features of workstations offering access to wide range of resources for professional translators.

Unlike other information behavior studies of professional translators, this article focuses on a broad range of resources, not just on dictionary use. It also identifies information problems associated not only with normal task activities, but also with translators' moving out of their zone of familiarity, i.e. their range of domain, language, and style expertise. The model of translator IB is potentially generalizable to other groups and both supports and expands other task‐related research.

The purpose of this study was to design a multitask learning model so that biomedical entities can be extracted without having any ambiguity from biomedical texts.

In the proposed automated bio entity extraction (ABEE) model, a multitask learning model has been introduced with the combination of single-task learning models. Our model used Bidirectional Encoder Representations from Transformers to train the single-task learning model. Then combined model's outputs so that we can find the verity of entities from biomedical text.

The proposed ABEE model targeted unique gene/protein, chemical and disease entities from the biomedical text. The finding is more important in terms of biomedical research like drug finding and clinical trials. This research aids not only to reduce the effort of the researcher but also to reduce the cost of new drug discoveries and new treatments.

As such, there are no limitations with the model, but the research team plans to test the model with gigabyte of data and establish a knowledge graph so that researchers can easily estimate the entities of similar groups.

As far as the practical implication concerned, the ABEE model will be helpful in various natural language processing task as in information extraction (IE), it plays an important role in the biomedical named entity recognition and biomedical relation extraction and also in the information retrieval task like literature-based knowledge discovery.

During the COVID-19 pandemic, the demands for this type of our work increased because of the increase in the clinical trials at that time. If this type of research has been introduced previously, then it would have reduced the time and effort for new drug discoveries in this area.

In this work we proposed a novel multitask learning model that is capable to extract biomedical entities from the biomedical text without any ambiguity. The proposed model achieved state-of-the-art performance in terms of precision, recall and F1 score.

Many academics prefer to think of education as “work” rather than “fun”. As a result, motivation in higher education rarely comes from the process itself. The author predicts this will change as the generation raised on the engagement of games no longer accepts the historical but unnecessary separation of fun and learning. The author offers the games world as an example of the process itself being motivating to the user. He ascribes this to “gameplay”, the techniques used by game designers to keep players engaged. The author suggests several ways to bring the motivation of gameplay into education, and predicts that gameplay will eventually become the criterion by which students choose their courses.

According to Nuttall's dictionary a profession is a “vocation, occupation or calling distinct from trade, and such as implies a measure of learning.” The same dictionary describes a vocation as a “designation or destination to a particular state or profession; summons; call; inducement; employment; calling; occupation or trade.”