No / 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 progress with 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.

Stock price prediction is of strong interest but a challenging task to both researchers and investors. Recently, sentiment analysis and machine learning have been adopted in stock price movement prediction. In particular, retail investors’ sentiment from online forums has shown their power to influence the stock market. In this paper, a novel system was built to predict stock price movement for the following trading day. The system includes a web scraper, an enhanced sentiment analyzer, a machine learning engine, an evaluation module, and a recommendation module. The system can automatically select the best prediction model from four state-of-the-art machine learning models (Long Short-Term Memory, Support Vector Machine, Random Forest, and Extreme Boost Gradient Tree) based on the acquired data and the models’ performance. Moreover, stock market lexicons were created using large-scale text mining on the Yahoo Finance Conversation boards and natural language processing. Experiments using the top 30 stocks on the Yahoo users’ watchlists and a randomly selected stock from NASDAQ were performed to examine the system performance and proposed methods. The experimental results show that incorporating sentiment analysis can improve the prediction for stocks with a large daily discussion volume. Long Short-Term Memory model outperformed other machine learning models when using both price and sentiment analysis as inputs. In addition, the Extreme Boost Gradient Tree (XGBoost) model achieved the highest accuracy using the price-only feature on low-volume stocks. Last but not least, the models using the enhanced sentiment analyzer outperformed the VADER sentiment analyzer by 1.96%.

Yes / The paper presents a text analytics framework that analyses online reviews to explore how consumer-perceived negativity corresponding to the supply chain propagates over time and how it affects car sales. In particular, the framework integrates aspect-level sentiment analysis using SentiWordNet, time-series decomposition, and bias-corrected least square dummy variable (LSDVc) – a panel data estimator. The framework facilitates the business community by providing a list of consumers’ contemporary interests in the form of frequently discussed product attributes; quantifying consumer-perceived performance of supply chain (SC) partners and comparing the competitors; and a model assessing various firms’ sales performance. The proposed framework demonstrated to the automobile supply chain using a review dataset received from a renowned car-portal in India. Our findings suggest that consumer-voiced negativity is maximum for dealers and minimum for manufacturing and assembly related features. Firm age, GDP, and review volume significantly influence car sales whereas the sentiments corresponding to SC partners do not. The proposed research framework can help the manufacturers in inspecting their SC partners; realising consumer-cited critical car sales influencers; and accurately predicting the sales, which in turn can help them in better production planning, supply chain management, marketing, and consumer relationships.

Yes / This exploratory research aims to investigate social media users’ expectations of information systems (IS) products that are conceived but not yet launched. It specifically analyses social media data from Twitter about forthcoming smartphones and smartwatches from Apple and Samsung, two firms known for their innovative gadgets. Tweets related to the following four forthcoming IS products were retrieved from 1st January 2020 to 30th September 2020: (1) Apple iPhone 12 (6,125 tweets), (2) Apple Watch 6 (553 tweets), (3) Samsung Galaxy Z Flip 2 (923 tweets), and (4) Samsung Galaxy Watch Active 3 (207 tweets). These 7,808 tweets were analysed using a combination of the Natural Language Processing Toolkit (NLTK) and sentiment analysis (SentiWordNet). The online community was quite vocal about topics such as design, camera and hardware specifications. For all the forthcoming gadgets, the proportion of positive tweets exceeded that of negative tweets. The most prevalent sentiment expressed in Apple-related tweets was neutral but in Samsung-related tweets was positive. Additionally, it was found that the proportion of tweets echoing negative sentiment was lower for Apple compared with Samsung. This paper is the earliest empirical work to examine the degree to which social media chatter can be used by project managers for IS development projects, specifically for the purpose of end-users’ expectation management.

Yes / The linear economic business model was deemed unsustainable, necessitating the emergence of the circular economy (CE) business model. Due to resource scarcity, increasing population, and high food waste levels, the food sector has been facing significant sustainability challenges. Small and medium-sized enterprises (SMEs), particularly those in the food sector, are making efforts to become more sustainable and to adopt new business models such as the CE, but adoption rates remain low. Industry 4.0 and its associated technological applications have the potential to enable CE implementation and boost business competitiveness. In the context of emerging economies facing significant resource scarcity constraints and limited technology availability, CE principles need to be adapted. CE could create a new job economy in emerging economies, bringing scale and a competitive advantage. This study explores the enablers of and barriers to Industry 4.0 adoption for CE implementation in fruit and vegetable SMEs in India from a resource-based perspective. The purpose is to develop an evidence-based framework to help inform theory and practice about CE implementation by SMEs in emerging economies. Fifteen semi-structured interviews were conducted with experts in food SMEs. The interview transcripts were first subjected to thematic analysis. The analysis was then complemented with sentiment and emotion analyses. Subsequently, hierarchical cluster analysis, k-means analysis, and linear projection analysis were performed. Among others, the findings suggest that Industry 4.0 plays a key role in implementing CE in SMEs in emerging economies such as India. However, there are specific enablers and barriers that need to be considered by SMEs to develop the resources and capabilities needed for CE competitive advantage.

Artificial intelligence (AI) and robotics are revolutionizing the traditional paradigm of business operations and transforming consumers’ experiences by promoting human–robot interaction in tourism and hospitality. Nonetheless, research related to customers’ experiences with robot-related services in this industry remains scant. This study thus seeks to investigate hotel customers’ experiences with service robots and how robot-based experiences shape customers’ satisfaction with hotel stays. Specifically, three research questions are addressed: (a) What are hotel customers’ primary concerns about robots and robot-related services? (b) Do hotel customers’ experiences with robotic services shape guests’ overall satisfaction? (c) How do service robots’ attributes affect guests’ forgiveness of robots’ service failure? This dissertation consists of three chapters. Chapter 1 introduces the overall research background. Chapter 2 answers the first two research questions by combining text mining and regression analyses; Chapter 3 addresses the third question by introducing social cognition into this investigation and performing an experiment. Overall, sentiment analyses uncovered customers’ generally positive experiences with robot services. Machine learning via latent Dirichlet allocation modeling revealed three key topics underlying hotel guests’ robot-related reviews—robots’ room delivery services, entertainment and catering services, and front office services. Regression analyses demonstrated that hotel robots’ attributes (e.g., mechanical vs. AI-assistant robots) and robot reviews’ characteristics (e.g., sentiment scores) can influence customers’ overall satisfaction with hotels. Finally, the experimental study verified uncanny valley theory and the existence of social cognition related to service robots (i.e., warmth and competence) by pointing out the interactive effects of robots’ anthropomorphism in terms of their facial expressions, voices, and physical appearance. These findings collectively yield a set of theoretical implications for researchers along with practical implications for hotels and robot developers. / Tourism and Sport

General purpose Twitter sentiment analysis is a novel field that is closely related to traditional Twitter sentiment analysis but slightly differs in some key aspects. The main difference lies in the fact that the novel approach considers the unfiltered public Twitter stream while most of the previous approaches often applied various filtering steps which are not feasible for many applications. Another goal is to yield more reliable results by only classifying a tweet as positive or negative if it distinctly consists of the respective sentiment and mark the remaining messages as uncertain. Traditional approaches are often not that strict. Within the course of this thesis it could be verified that the novel approach differs significantly from the traditional approach. Moreover, the experimental results indicated that the archetypical approaches could be transferred to the new domain but the related domain data is consistently sub par when compared to high quality in-domain data. Finally, the viability of the best classification algorithm could be qualitatively verified in a real-world setting that was also developed within the course of this thesis.

The interest of researchers and practitioners in e-Word-of-Mouth has accelerated with the rise of social media platforms. In the last decade it became more apparent that customers are not merely recipients of product and brand information, but also active participants in shaping product and brand perceptions. However, still no comprehensive understanding of customers’ desire to engage in positive and negative e-Word-of-Mouth has been found. Therefore, this study focuses on Customer Engagement in order to provide knowledge on what makes customers engage in e-Word-of-Mouth. We narrow our study to New Product Introductions due to their crucial role in both business success and failure. In order to study this topic, quantitative methodology using big data analysis of around 20 millions tweets in total, with text analysis of around three million items, obtained from Twitter has been applied. The findings indicate that Brand Benefits and Innovativeness Level are two important aspects that impact Attitudinal Engagement which enables Customer Engagement Behavior.

Many companies strive to make data-driven decisions. To achieve this, they need to explore new tools for Business Intelligence. The aim of this study was to examine the performance and usability of aspect-based sentiment analysis as a tool for Business Intelligence in E-commerce. The study was conducted in collaboration with Ellos Group AB which supplied anonymous customer feedback data. The implementation consists of two parts, aspect extraction and sentiment classification. The f irst part, aspect extraction, was implemented using dependency parsing and various aspect grouping techniques. The second part, sentiment classification, was implemented using the language model KB-BERT, a Swedish version of the BERT model. The method for aspect extraction achieved a satisfactory precision of 79,5% but only a recall of 27,2%. Moreover, the result for sentiment classification was unsatisfactory with an accuracy of 68,2%. Although the results underperform expectations, we conclude that aspect-based sentiment analysis in general is a great tool for Business Intelligence. Both as a means of generating customer insights from previously unused data and to increase productivity. However, it should only be used as a supportive tool and not to replace existing processes for decision-making. / Många företag strävar efter att fatta datadrivna beslut. För att åstadkomma detta behöver de utforska nya metoder för Business Intelligence. Syftet med denna studie var att undersöka prestandan och användbarheten av aspektbaserad sentimentanalys som ett verktyg för Business Intelligence inom e-handeln. Studien genomfördes i samarbete med Ellos Group AB som tillhandahöll data bestående av anonym kundfeedback. Implementationen består av två delar, aspektextraktion och sentimentklassificering. Aspektextraktion implementerades med hjälp av dependensparsning och olika aspektgrupperingstekniker. Sentimentklassificering implementerades med hjälp av språkmodellen KB-BERT, en svensk version av BERT. Metoden för aspektextraktion uppnådde en tillfredsställande precision på 79,5% men endast en recall på 27,2%. Resultatet för sentimentklassificering var otillfredsställande med en accuracy på 68,2%. Även om resultaten underpresterar förväntningarna drar vi slutsatsen att aspektbaserad sentimentanalys i allmänhet är ett bra verktyg för Business Intelligence. Både som ett sätt att generera kundinsikter från tidigare oanvända data och som ett sätt att öka produktiviteten. Det bör dock endast användas som ett stödjande verktyg och inte ersätta befintliga processer för beslutsfattande.

The rapid development and disitribution of COVID-19 vaccines have sparked diverse public reactions globally, often reflected through social media platförms like Twitter. This study aims to analyze the sentiment andd public discourse surrounding COVID-19 vaccines on Twitter, utilizing advanced text classification techniques to navigare the vast, unstructured nature of sicial media dfata. By implementing sentiment analysis, the research categoizes tweets into positive, negative, and neutral sentiments to gauge public opinion more effectively. In-depth analysis thorugh topic modelingtecniques helped identify seven key topicvs influencing public sentiment including aspects related to efficiacy, logisticl challenges, safety concens, and personal experiences, each varying in prominence depending on the country, as well as the specific timeline of vaccine deployment. Additionally, this study explorers geographical variations in sentiment, notig significant differences in public opinion across different countries. These variations could be tied to local cultural, social, and political contexts. Reults from this study show a polarized response towards vaccination, with significant discourse clusers showing either strong supprt for or resistance against the COVID-19 vaccination efforts. This polarization is further pronounced by the logistical challenges and trust issues related to vaccine science, particularly emphasized in tweets from couintries with lower vaccine acceptance rates. This sentiment analysis on Twitter offers valuable insights into the public's perception and acceptancce of COVID-19 vaccines, providing a useful tool for policymakers and public health officials to understand and address publiv concerns effectively. By identifying and understanding the key factors influencing vaccine sentiment, tageted communication strategies can be developed to enhance publiv engagement and vaccine uptake.