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%.

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

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.

By leveraging historical data together with machine learning algorithms, marketers can predict how new campaigns are likely to perform before launch. This approach can save time and resources and can help marketers optimize campaigns in current time through adjustments to increase return on investment (ROI) and reach the right target group. The objective of this thesis is to develop a predictive model through the application of feature selection techniques to assess the likability of a campaign. This study aims to identify the key features that significantly influence campaign likability and to quantify their impact. The task has been approached as a regression problem, with the objective of examine what predictors drives the liking of a campaign. The study implemented four methods for feature selection, recursive feature elimination with cross validation conjucted with random forest, lasso regression, ridge regression and decision trees. Further, to model, the following machine learning algorithms were employed: linear regression, ridge regression with cross validation, lasso regression with cross validation, elastic net with cross validation, kernel ridge regression and support vector regression. Based on the machine learning algorithm and the available data, the results indicate that the set of features generated by recursive feature elimination with cross validation combined with random forest was the most prominent and the algorithm support vector regression generated the best models. / Genom att använda historisk data tillsammans med maskininlärningsalgoritmer kan marknadsförare prediktera hur nya kampanjer sannolikt kommer att prestera innan de lanseras. Denna strategi kan spara tid och resurser och hjälpa marknadsförare att optimera kampanjer i realtid genom justeringar för att öka avkastningen på investeringen och nå rätt målgrupp. Målet med denna avhandling är att utveckla en prediktiv modell genom tillämpning av metodiker för variabelselektion för att bedöma sannolikheten för att en kampanj kommer att vara omtyckt. Denna studie syftar till att identifiera de nyckelvariabler som signifikant påverkar kampanjens popularitet och kvantifiera deras påverkan. Uppgiften behandlas som ett regressionsproblem för att identifiera vilka prediktorer som bidrar till ett positivt helhetsintryck av en kampanj. Studien implementerade fyra metoder för urval av variableselektion: rekursiv variabelselektion med korsvalidering kombinerad med random forest, lasso-regression, ridge-regression och beslutsträd. Dessutom användes följande maskininlärningsalgoritmer för modellering: linjär regression, ridge regression med korsvalidering, lasso regression med korsvalidering, elastiskt nät med korsvalidering, kernel ridge regression och stödvektorsregression. Baserat på maskininlärningsalgoritmerna och det tillgängliga datat indikerar resultaten att uppsättningen av funktioner genererad av rekursiv variabelselektion med korsvalidering kombinerad med random forest var mest framträdande och att algoritmen stödvektorregression genererade de bästa modellerna.

In our study, we conduct a comparative analysis between dictionary-based sentiment analysis and entailment zero-shot text classification for German sentiment analysis. We evaluate the performance of a selection of dictionaries on eleven data sets, including four domain-specific data sets with a focus on historic German language. Our results demonstrate that, in the majority of cases, zero-shot text classification outperforms general-purpose dictionary-based approaches but falls short of the performance achieved by specifically fine-tuned models. Notably, the zero-shot approach exhibits superior performance, particularly in historic German cases, surpassing both general-purpose dictionaries and even a broadly trained sentiment model. These findings indicate that zero-shot text classification holds significant promise as an alternative, reducing the necessity for domain-specific sentiment dictionaries and narrowing the availability gap of off-the-shelf methods for German sentiment analysis. Additionally, we thoroughly discuss the inherent trade-offs associated with the application of these approaches.

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.