Emotion classification can be a powerful tool to derive narratives from social media data. Traditional machine learning models that perform emotion classification on Indonesian Twitter data exist but rely on closed-source features. Recurrent neural networks can meet or exceed the performance of state-of-the-art traditional machine learning techniques using exclusively open-source data and models. Specifically, these results show that recurrent neural network variants can produce more than an 8% gain in accuracy in comparison with logistic regression and SVM techniques and a 15% gain over random forest when using FastText embeddings. This research found a statistical significance in the performance of a single-layer bidirectional long short-term memory model over a two-layer stacked bidirectional long short-term memory model. This research also found that a single-layer bidirectional long short-term memory recurrent neural network met the performance of a state-of-the-art logistic regression model with supplemental closed-source features from a study by Saputri et al. [8] when classifying the emotion of Indonesian tweets.

The emergence of the Internet of Things (IoT), cloud computing, cyber-physical systems, system integration, big data, and data analytics for Industry 4.0 have transformed the world of traditional manufacturing into an era of smart manufacturing (SM). Smart manufacturing’s central focus is to process real-time IoT data and leverage advanced analytical approaches to detect abnormal behaviors. Social smart manufacturing applies analytics tools to empower decision makers and minimize duplication by executing the repetitive data processing work more consistently and precisely than can be done by a human operator. In smart manufacturing, the majority of industrial data is imbalanced. However, most traditional machine learning algorithms tend to be biased toward the majority class and under-represent the minority class. This research proposes a model selection architecture to automate the procedure of preprocessing input data and selecting the best combination of algorithms for anomaly detection. This design will play an essential role in producing high-quality products and improving quality control and business processes in diverse applications including predictive maintenance and fault detection. The framework is transferrable to any smart manufacturing task in the supervised learning domain.

Receiving and acting on customer input is essential to sustaining and growing any service organization, particularly a small family business whose livelihood depends on strong relationships with its customers. The competitive advantage offered by advanced analytical approaches for supporting decisions is not trivial, and enterprises across virtually all domains of society are investing heavily in this emerging discipline. Natural Language Processing (NLP) is a subset of computer science that employs computational approaches to analyze human language; it is effective at extracting insight from text data but frequently requires large corpora to train its models, in the scale of thousands or millions of documents. This restricts its accessibility to those large enterprises with the capability to capture, store, manage, and analyze such corpora. This research explores a pilot study that applies NLP approaches, specifically topic modeling and large language models (LLM), to assist a small, family-owned business in assessing its strengths and weaknesses based on customer reviews. The relevant corpora of online Facebook, Google Reviews, TripAdvisor, and Yelp reviews is far smaller than ideal, numbering only in the hundreds. Results demonstrate that coherent and actionable insights from big-data approaches are obtainable and that small organizations are not automatically excluded from the benefits of these advanced analytical approaches, with complementary employment of both topic modeling and LLM presenting the greatest potential for similarly-positioned organizations to exploit.

Business, political, and other social structures create strong motivation to understand the attitudes, motivations, feelings, and emotions of a population of interest. Social media is a rich source of self-disclosed information by individuals from all walks of life about virtually every domain of the human experience, but the vast quantity of data is impossible to effectively analyze without advanced natural language processing algorithms. This research creates a transfer learning based emotion classification model for Indonesian language Twitter data. Transfer learning consists of two steps: pre-training and fine tuning. Three variations of Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT) are tested with hyperparameters tuned via designed experiment. The top IndoBERT model, tested on an open source corpus of 4,401 labeled Indonesian Tweets, outperforms all known prior studies with an F1 score of approximately 0.791. Additionally, this research explores the relationship between training set size and model validity for fine tuning of the transfer learning models; datasets ranging from 100 to 3900 observations are trained and then validated on five unique test sets. Results indicate that as few as 1000 observations can obtain results comparable to using the full training corpus.

The broad context of this literature review is the connected manufacturing enterprise, characterized by a data environment such that the size, structure and variety of information strain the capability of traditional software and database tools to effectively capture, store, manage and analyze it. This paper surveys and discusses representative examples of existing research into approaches for feature set reduction in the big data environment, focusing on three contexts: general industrial applications; specific industrial applications such as fault detection or fault prediction; and data reduction. The conclusion from this review is that there is room for research into frameworks or approaches to feature filtration and prioritization, specifically with respect to providing quantitative or qualitative information about the individual features in the dataset that can be used to rank features against each other. A byproduct of this gap is a tendency for analysts not to holistically generalize results beyond the specific problem of interest, and, related, for manufacturers to possess only limited knowledge of the relative value of smart manufacturing data collected.