Does Digitalisation Promote the Servicification of Manufacturing? Firm‐Level Evidence From China

TTwitter has allowed textual data to be collected using Text Mining and Sentiment Analysis techniques in the age of social media in which user-generated content becomes redundant. However, due to some inconsistencies, Text Cleaning plays an important role before Text Mining and Sentiment Analysis techniques can be conducted. Hence, this study is conducted to discover the capabilities of Text Cleaning, Text Mining and Sentiment Analysis in three different data mining tools: SAS® Text Miner (proprietary text mining tool), Python and R programming (open-source text mining tools). These data mining tools were used to conduct the Text Cleaning, Text Mining and Sentiment Analysis and their capabilities such as features, functions and characteristics were evaluated and investigated, to conduct this comparison study. All the proposed research objectives were met successfully even with the given limitation. A movie critique Dictionary is one of the major theoretical implications of this research. Based on our analysis and results, developers or educational practitioners can discover what is important and what is unimportant when conducting Text Mining and Sentiment Analysis. They will also obtain insights and guidance on how to conduct Text Mining and Sentiment Analysis using SAS Enterprise Miner, Python and R.

A theme analysis model integrating text mining and sentiment analysis has emerged as a powerful tool for understanding English and American literary works. By employing techniques such as topic modeling, keyword extraction, and sentiment analysis, this model can identify recurring themes, motifs, and emotional tones within texts. Through text mining, it extracts key concepts and topics, while sentiment analysis discerns the underlying emotions conveyed by the authors. By combining these approaches, researchers can uncover deeper insights into the thematic elements and cultural contexts of English and American literature. This paper explores the application of text mining and sentiment analysis techniques to analyze a dataset comprising American literary works. With computational methods such as bi-gram analysis, multimodal feature extraction, and sentiment analysis using the Bi-gram Multimodal Sentimental Analysis (Bi-gramMSA) approach. With the proposed Bi-gramMSA model the multimodal features in the American Literature are examined to investigate the thematic, emotional, and multimodal aspects of the literature. Through our analysis, we uncover significant bi-grams, extract multimodal features, and assess sentiment distribution across the texts. The results highlight the effectiveness of these computational methodologies in uncovering patterns, sentiments, and features within the literary corpus. The proposed Bi-gramMSA model achives a higher score for the different scores in the Chinese Literature.

Text mining and sentiment analysis have received huge attention recently, specially because of the availability of vast data in form of text available on social media, e-commerce websites, blogs and other similar sources. This data is usually unstructured and contains noise, therefore the task of gaining information is complex and expensive. There is a growing need for developing different methodologies and models for efficiently processing the texts and extracting apt information. One way to extract information is text mining and sentiment analysis, that include: data acquisition, data pre-processing and normalization, feature extraction and representation, labelling, and finally the application of various Natural Language Processing (NLP) and machine learning algorithms. This paper provides an overview of different methods used in text mining and sentiment analysis elaborating on all subtasks.

Emotions are highly dynamic and social in nature. Traditional approaches to studying emotion expression face obstacles such as substantial time investments, susceptibility to human biases, and limited capacity to capture nuanced emotional patterns. To address these challenges, this research leveraged text mining and sentiment analysis to explore the dynamic patterns of emotion expression within the context of mother‐child interactions. We analyzed 8,841 conversation transcripts involving 1,462 mother‐child dyads, sourced from the Child Language Data Exchange System. Polarity scores were calculated and analyzed to uncover the temporal patterns of mother and child emotional sentiment. Our findings revealed that mothers tended to exhibit heightened levels of positive emotion at the beginning and conclusion of conversations, whereas children displayed a more linear positive trend. Using model‐based cluster analysis, we identified two distinct clusters of mothers characterized by varying degrees of emotion expression variation and two clusters of children showing different rates of elevation in positive emotion. At the dyadic level, the differences between mother and child polarity scores varied as a function of time, with an increase of difference from the beginning to the 20th percentile point, a decrease until the 90th percentile, and then an increase again towards the end of the conversation. This study demonstrates the utility of text mining and sentiment analysis in developmental studies, particularly in the context of parent‐child interactions. The findings hold informative implications for interventions that focus on fostering healthy parent‐child relationships.

PurposeThe current study employs text mining and sentiment analysis to identify core banking service attributes and customer sentiment in online user-generated reviews. Additionally, the study explains customer satisfaction based on the identified predictors.Design/methodology/approachA total of 32,217 customer reviews were collected across 29 top banks on bankbazaar.com posted from 2014 to 2021. In total three conceptual models were developed and evaluated employing regression analysis.FindingsThe study revealed that all variables were found to be statistically significant and affect customer satisfaction in their respective models except the interest rate.Research limitations/implicationsThe study is confined to the geographical representation of its subjects' i.e. Indian customers. A cross-cultural and socioeconomic background analysis of banking customers in different countries may help to better generalize the findings.Practical implicationsThe study makes essential theoretical and managerial contributions to the existing literature on services, particularly the banking sector.Originality/valueThis paper is unique in nature that focuses on banking customer satisfaction from online reviews and ratings using text mining and sentiment analysis.

Using text mining and sentiment analysis for online forums hotspot detection and forecast

The growing incidents of counterfeiting and associated economic and health consequences necessitate the development of active surveillance systems capable of producing timely and reliable information for all stake holders in the anti-counterfeiting fight. User generated content from social media platforms can provide early clues about product allergies, adverse events and product counterfeiting. This paper reports a work in progresswith contributions including: the development of a framework for gathering and analyzing the views and experiences of users of drug and cosmetic products using machine learning, text mining and sentiment analysis, the application of the proposed framework on Facebook comments and data from Twitter for brand analysis, and the description of how to develop a product safety lexicon and training data for modeling a machine learning classifier for drug and cosmetic product sentiment prediction. The initial brand and product comparison results signify the usefulness of text mining and sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provides a useful tool for users, product manufacturers, regulatory and enforcement agencies to monitor brand or product sentiment trends in order to act in the event of sudden or significant rise in negative sentiment.

Investors have to deal with an increasing amount of information in order to make beneficial investment decisions. Thus, text mining is often applied to support the decision-making process by predicting the stock price impact of financial news. Recent research has shown that there exists a relation between news article sentiment and stock prices. However, this is not considered by previous text mining studies. In this paper, we develop a novel two-stage approach that connects text mining with sentiment analysis to predict the stock price impact of company-specific news. We find that the combination of text mining and sentiment analysis improves forecasting results. Additionally, a higher accuracy can be achieved by using finance-related word lists for sentiment analysis instead of a generic dictionary.

This study aims to test, examine, and validate text-based human-machine knowledge transfer (KT) by comparing it with human-human KT. The online discussion experiment was carried out via WhatsApp group chats. Chat sentiment was determined using text mining and sentiment analysis and then compared with the respondent's understanding of the knowledge obtained from interviews. The results have shown that human-machine KT is close to human-human KT. By analyzing the correlation coefficient between the two, it is proven that sentiment indicates an understanding of knowledge. Positive sentiment shows similar or in-line understanding between the source and recipient of knowledge and indicates the achievement of KT objectives. Neutral sentiment indicates incomprehension due to the failure of KT. Meanwhile, negative sentiment is ambiguous; it may indicate an incomprehension or a misunderstanding of the knowledge received. This study contributes to the area of knowledge and sentiments, showing that the effectiveness of text-based KT activity can be identified using the sentiment analysis approach.

Purpose There is a strong need for companies to monitor customer-generated content of social media, not only about themselves but also about competitors, to deal with competition and to assess competitive environment of the business. The purpose of this paper is to help companies with social media competitive analysis and transformation of social media data into knowledge creation for decision-makers, specifically for app-based food delivery companies. Design/methodology/approach Three online app-based food delivery companies, i.e. Swiggy, Zomato and UberEats, were considered in this study. Twitter was used as the data collection platform where customer’s tweets related to all three companies are fetched using R-Studio and Lexicon-based sentiment analysis method is applied on the tweets fetched for the companies. A descriptive analytical method is used to compute the score of different sentiments. A negative and positive sentiment word list is created to match the word present on the tweets and based on the matching positive, negative and neutral sentiments score are decided. The sentiment analysis is a best method to analyze consumer’s text sentiment. Lexicon-based sentiment classification is always preferable than machine learning or other model because it gives flexibility to make your own sentiment dictionary to classify emotions. To perform tweets sentiment analysis, lexicon-based classification method and text mining were performed on R-Studio platform. Findings Results suggest that Zomato (26% positive sentiments) has received more positive sentiments as compared to the other two companies (25% positive sentiments for Swiggy and 24% positive sentiments for UberEats). Negative sentiments for the Zomato was also low (12% negative sentiments) compared to Swiggy and UberEats (13% negative sentiments for both). Further, based on negative sentiments concerning all the three food delivery companies, tweets were analyzed and recommendations for business provided. Research limitations/implications The results of this study reveal the value of social media competitive analysis and show the power of text mining and sentiment analysis in extracting business value and competitive advantage. Suggestions, business and research implications are also provided to help companies in developing a social media competitive analysis strategy. Originality/value Twitter analysis of food-based companies has been performed.

PurposeThe increasing competition among higher education institutions (HEI) has led students to conduct a more in-depth analysis to choose where to study abroad. Since students are usually unable to visit each HEIs before making their decision, they are strongly influenced by what is written by former international students (IS) on the internet. HEIs also benefit from such information online. The purpose of this paper is to provide an understanding of the drivers of HEIs success online.Design/methodology/approachDue to the increasing amount of information published online, HEIs have to use automatic techniques to search for patterns instead of analysing such information manually. The present paper uses text mining (TM) and sentiment analysis (SA) to study online reviews of IS about their HEIs. The paper studied 1938 reviews from 65 different business schools with Association to Advance Collegiate Schools of Business accreditation.FindingsResults show that HEIs may become more attractive online if they financially support students cost of living, provide courses in English, and promote an international environment.Research limitations/implicationsDespite the use of a major platform with a broad number of reviews from students around the world, other sources focussed on other types of HEIs may have been used to reinforce the findings in the current paper.Originality/valueThe study pioneers the use of TM and SA to highlight topics and sentiments mentioned in online reviews by students attending HEIs, clarifying how such opinions are correlated with satisfaction. Using such information, HEIs’ managers may focus their efforts on promoting international attractiveness of their institutions.

Social media is now regarded as the most valuable source of data for trend analysis and innovative business process reengineering preferences. Data made accessible through social media can be utilized for a variety of purposes, such as by an entrepreneur who wants to learn more about the market they intend to enter and uncover their consumers’ requirements before launching their new products or services. Sentiment analysis and text mining of telecommunication businesses via social media posts and comments are the subject of this study. A proposed framework will be utilized as a guideline, and it will be tested for sentiment analysis. Lexicon-based sentiment categorization is used as a model training dataset for a supervised machine learning support vector machine. The result is very promising. The accuracy and the quantity of the true sentiments it can detect are compared. This result signifies the usefulness of text mining and sentiment analysis on social media data, while the use of machine learning classifiers for predicting sentiment orientation provides a useful tool for operations and marketing departments. The availability of large amounts of data in this digitally active society is advantageous for sectors such as the telecommunication industry. These companies can be two steps ahead with their strategy and develop a more cohesive company that can make customers happier and mitigate problems easily with the use of text mining and sentiment analysis for further adopting innovative business process reengineering for service improvements within the telecommunications industry.

This study employs the perspective of global value chain (GVC) to address the workforce development (WFD) strategies conducted by Taiwanese OEMs in China for supporting their GVC upgrading. According to the case-based empirical analysis, five major characteristics of their WFD strategies are identified: 1) Consideration of the imbalance between skills supply and demand for GVC upgrading in China; 2) Inclusion of training industry-specific skills as per international standards, 3) Emphasis on developing “soft skills”; 4) Specific training for key bottleneck positions required by GVC upgrading; 5) Establishment of innovative corporate career development initiatives. This paper contributes to the literature by promoting better understanding of the WFD strategies by Taiwanese OEMs in China as well as examining the critical role of these strategies in facilitating GVC upgrading. Further, since a variety of enterprises interviews were conducted, it answers to a recent call for using first-hand information to analyze relevant issues, providing constructive implications for other Taiwanese OEMs to overcome the critical skilled labor shortage that may hinder their GVC upgrading.

Predicting stock market behavior is a challenge that has been studied and presented several solutions in the literature. Due to technological advances, methodologies have emerged and allowed new approaches to this problem in recent years. Text mining and sentiment analysis have been widely applied in this area. On the other hand, classic solutions as time series analysis continue to be used alone or with new methods. There is still no literature review of the joint use of these methods. In this way, this study presents a systematic review with 57 selected papers using time series, text mining, and sentiment analysis applied to predict financial stock market behavior. Through this research, it was observed that the use of data from social media and internet sites is a compound source of information, providing a better prediction. However, the selection and combination of these data in a relevant way are still limitations found in the proposed models.

The interaction between a mother and child stands as one of the most profound and intricate human connections, weaving a rich tapestry of behavioral and emotional bonds during the formative years. Although mother–child interactions have received substantial attention in the developmental science literature, few studies have tapped into the extensive corpus of speech data available to uncover the nuances of these interactions across developmental stages. This study applied text mining and sentiment analysis on narratives extracted from mother–child conversations to identify the developmental trend of mother–child interactions from early to middle childhood. The results, based on three key areas of dyadic interactions, demonstrated a shift toward more balanced turn-taking dynamics and linguistic congruence as children age. Also, there was a significant interdependence of mother and child expressed emotions across time. Further investigation of the dyadic emotionality revealed a nonlinear effect of mother-expressed emotion on child-expressed emotion: mother-expressed negative emotions followed a cubic-like pattern, while positive emotions followed a mild quadratic trend. Taken together, the findings of this study present a picture of progressive augmentation of mother–child synchrony over time.