AN INTELLIGENT TEXT TO EMOTION TWITTER SENTIMENT ANALYSIS MODEL USING BIDIRECTIONAL LSTM NETWORKS

This study aims to develop high-performing Machine Learning and Deep Learning models in predicting hospital length of stay (LOS) while enhancing interpretability. We compare performance and interpretability of models trained only on structured tabular data with models trained only on unstructured clinical text data, and on mixed data. The structured data was used to train fourteen classical Machine Learning models including advanced ensemble trees, neural networks and k-nearest neighbors. The unstructured data was used to fine-tune a pre-trained Bio Clinical BERT Transformer Deep Learning model. The structured and unstructured data were then merged into a tabular dataset after vectorization of the clinical text and a dimensional reduction through Latent Dirichlet Allocation. The study used the free and publicly available Medical Information Mart for Intensive Care (MIMIC) III database, on the open AutoML Library AutoGluon. Performance is evaluated with respect to two types of random classifiers, used as baselines. The best model from structured data demonstrates high performance (ROC AUC = 0.944, PRC AUC = 0.655) with limited interpretability, where the most important predictors of prolonged LOS are the level of blood urea nitrogen and of platelets. The Transformer model displays a good but lower performance (ROC AUC = 0.842, PRC AUC = 0.375) with a richer array of interpretability by providing more specific in-hospital factors including procedures, conditions, and medical history. The best model trained on mixed data satisfies both a high level of performance (ROC AUC = 0.963, PRC AUC = 0.746) and a much larger scope in interpretability including pathologies of the intestine, the colon, and the blood; infectious diseases, respiratory problems, procedures involving sedation and intubation, and vascular surgery. Our results outperform most of the state-of-the-art models in LOS prediction both in terms of performance and of interpretability. Data fusion between structured and unstructured text data may significantly improve performance and interpretability.

With the meteoric development of the big data, internet of things (IoT), the very first question is about how to scout the massive volume of data from heterogeneous data sources. The modern tools and techniques commonly used in successful transmuting of well-structured data into business intelligence (BI) clearly don't work when started on unlabeled data. It is needed to have effective and efficient mapping processes to transform unstructured text data to structured text data with assigned categories to make rapid decisions. With the support of modern automatic text mining tools, the decision-making process can now be done efficiently and cost-effectively. The deep learning concept has attained state-of-the-art results. The mapping operation of unstructured text data to classified structured text data will symbolize unstructured data as renewable assets that are well arranged, useful, and meaningful to serve organizational operations. This chapter presented a comparison of conventional and deep learning methods for unstructured text data classification and its challenges.

Text classification which is an integral part of text mining has caught much attention in various industries and fields recently. The ability is in assigning text documents to one or more pre-defined categories based on content similarity. While most of application of text classification focuses on document level, question classification works at much granular level such as sentence and phrase. There have been numerous studies on question classification in accordance to Bloom taxonomy in assessments to measure cognitive level of learners in higher learning institutions. But it has not been effective yet to resolve overlapping issue of Bloom taxonomy verb keywords being assigned to more than one category of Bloom taxonomy. The presence of this poses a problem in respect of classifying a particular question into a right category of Bloom taxonomy. And feature extraction plays an important role in improving the accuracy of classifier such as Support Vector Machine in question classification. Much of the past related research work focus on feature extraction methods such as bag of word (BOW) and syntactic analysis to classify questions and to address the issue, an improvement in feature extraction is needed. In view of this, this study proposes an integrated approach in feature extraction involving semantic aspect in classifying questions in accordance to Bloom taxonomy. Support Vector Machine classifier is used as it is well known for its high accuracy in text classification. With all this in place, an improved accuracy in classifying questions in accordance to Bloom taxonomy can be expected.

League of Legends: Wild Rift is a mobile game with more than 48 million downloads worldwide. The game publishers could earn profit from selling item in the game (in-app purchases). Performance of player and players' impressions during the first week usually determine whether players would made in-app purchases or not. Therefore, it is important to understand the player opinions so that the game publisher could encourage the players to increase the in-app purchases. Therefore, this research utilized sentiment analysis to study the player opinions about the League of Legends: Wild Rift game based on the reviews given by the players on the Google Play Store. The sentiment analysis was applied by using Naive Bayes Classifier (NBC) algorithm which was well known for achieving good accuracy in the sentiment analysis task. In addition, data preprocessing and feature extraction should be carried out properly to increase the accuracy of the classifier. Therefore, this research investigated the impact of using stemming and transformation of informal words into formal words in the preprocessing stages, then compared two feature extraction algorithms, namely Term Frequency – Inverse Document Frequency (TF-IDF) and Bag of Words (BOW). From the experiment, it was found that the use of stemming could decrease the accuracy of the classifier, but the use of transformation of non-standard words into standard words could improve the performance of the classifier, for both feature extractions, BOW and TF-IDF. In this case, BOW feature extraction was able to achieve better performance, compared to TF-IDF. The best model was achieved when not using stemming, applying the transformation of informal words into formal words, and using BOW bigram feature extraction, with the accuracy of 79,3%, precision of 82.10%, recall of 83.50%, and f1-score of 82,8.10%.

The evaluation of the learning process is an effective way to realize personalized online learning. Real-time evaluation of learners’ cognitive level during online learning helps to monitor learners’ cognitive state and adjust learning strategies to improve the quality of online learning. However, most of the existing cognitive level evaluation methods use manual coding or traditional machine learning methods, which are time-consuming and laborious. They cannot fully mine the implicit cognitive semantic information in unstructured text data, making the cognitive level evaluation inefficient. Therefore, this study proposed the bidirectional gated recurrent convolutional neural network combined with an attention mechanism (AM-BiGRU-CNN) deep neural network cognitive level evaluation method, and based on Bloom’s taxonomy of cognition objectives, taking the unstructured interactive text data released by 9167 learners in the massive open online course (MOOC) forum as an empirical study to support the method. The study found that the AM-BiGRU-CNN method has the best evaluation effect, with the overall accuracy of the evaluation of the six cognitive levels reaching 84.21%, of which the F1-Score at the creating level is 91.77%. The experimental results show that the deep neural network method can effectively identify the cognitive features implicit in the text and can be better applied to the automatic evaluation of the cognitive level of online learners. This study provides a technical reference for the evaluation of the cognitive level of the students in the online learning environment, and automatic evaluation in the realization of personalized learning strategies, teaching intervention, and resources recommended have higher application value.

Unstructured text data is very important in many applications because it reflects the thought of the people who create this data. However, it is difficult to realize the latent information as it was hidden on the unstructured text data. This paper proposes a latent learning method to construct the lexical structure to constitute the relations between the latent meaning and words. The established lexical structure derived the useful information from unstructured text data and this information and this information can be used for various application. This paper describes how to predict a rating from user-written reviews which is one of unstructured text data. And it also provides visualization information of the semantic lexical structures as the result of analysis. As a result, the proposed method easily quantifies the semantic relations of words and it shows good performance on prediction of ratings from unstructured text data. The proposed method can contribute to analyzing the unstructured text data in various perspectives on latent meaning of words.

Purpose This study explores how a privacy-preserving local large language model (Local-LLM) assists researchers in evaluating unstructured text data quality, early in the analysis process. It aims to accurately and effectively determine if a Local-LLM can evaluate the quality of open-ended survey responses compared to human coders during the initial analysis of unstructured survey data. Design/methodology/approach A Python tool using Llama 3.2 and 3.3 classified 604 survey responses. Human-coded sentiment labels served as a baseline; model performance was assessed with confusion matrices, F1, Cohen’s κ and Gwet’s AC1. All processing was offline to protect data privacy. Findings Llama 3.3 achieved top performance (F1 ≈ 0.97, AC1 ≈ 0.97), while Llama 3.2 also excelled on consumer hardware. Automated sentiment analysis reduced processing time from 4 h to 8 min and identified short responses that manual reviewers might miss, improving speed and data quality. Research limitations/implications The approach’s reliability beyond English or on longer narratives remains to be examined in future work. Practical implications The Local-LLMs allow researchers to rapidly filter and assess large volumes of unstructured text data before conducting deeper analysis. Originality/value This study demonstrates that Local-LLM’s semantic features can be used for sentiment analysis as a scalable, cost-effective and rapid method for evaluating unstructured text data, aligning with the research questions before analysis. It also illustrates how to use zero-shot prompting to interact with Ollama via Local-LLMs, simplifying AI integration for non-technical researchers.

Sentiment analysis plays a pivotal role in understanding the sentiments and opinions expressed in textual data, offering valuable insights into various domains, including tourism. In this study, we present a comprehensive review of sentiment analysis techniques applied to tourism reviews using machine learning algorithms. The abundance of user-generated content on tourism platforms has made sentiment analysis an indispensable tool for businesses and researchers alike. By leveraging machine learning algorithms, researchers can extract sentiments from vast amounts of textual data efficiently and accurately. This review outlines the key methodologies and approaches utilized in sentiment analysis of tourism reviews. It discusses preprocessing techniques such as text tokenization, stop-word removal, and stemming, which are crucial for preparing textual data for analysis. Furthermore, it examines various machine learning algorithms employed for sentiment classification, including Naive Bayes, Support Vector Machines, and Recurrent Neural Networks. Additionally, the review delves into feature extraction methods such as bag-of-words, TF-IDF, and word embeddings, highlighting their impact on sentiment analysis accuracy. Moreover, it explores the challenges and limitations associated with sentiment analysis in the tourism domain, such as sarcasm detection and language nuances.

The Mel-Frequency Cepstral Coefficients (MFCC) feature extraction method is a leading approach for speech feature extraction and current research aims to identify performance enhancements. One of the recent MFCC implementations is the Delta-Delta MFCC, which improves speaker verification. In this paper, a new MFCC feature extraction method based on distributed Discrete Cosine Transform (DCT-II) is presented. Speaker verification tests are proposed based on three different feature extraction methods including: conventional MFCC, Delta-Delta MFCC and distributed DCT-II based Delta-Delta MFCC with a Gaussian Mixture Model (GMM) classifier.

In this paper, we consider the problem of remote sensing image classification, in which feature extraction and feature coding are critical steps. Various feature extraction methods aim at an abstract and discriminative image representation. Most of them are either theoretically too complex or practically infeasible to compute for large datasets. Motivated by this observation, we propose a simple yet efficient feature extraction method within the bag-of-words (BoW) framework. It has two main innovations. First and most interestingly, this method does not need any complex local feature extraction; instead, it uses directly the pixel values from a local window as low level features. Second, in contrast to many unsupervised feature learning methods, a random dictionary is applied to feature space quantization. The advantage of a random dictionary is that it does not need the time-consuming process of dictionary learning yet without a significant loss of classification accuracy. These two novel improvements over state-of-the-art methods significantly reduce the computational time and enable it scalable to a large data volume. An extensive experimental evaluation has been performed and compared with other feature extraction methods. It is demonstrated that our feature extraction method is quite competitive and can achieve rather promising performance figures for both optical and SAR satellite images.

The availability of massive sets of unstructured data has opened up new opportunities for businesses to gain meaningful insights into what is currently happening in their organization and market place. Unfortunately, unstructured data is often challenging to work with, as it requires specialized toolsets, techniques, knowledge and skills to engage with the data. This study used a systematic review (SLR) process to explore the current and conversant state of affairs in the field of text mining (TM) techniques that exist for the processing of natural language in unstructured text datasets across a broad expanse of applications and domains. A comprehensive literature search yielded 1022 eligible articles from five prominent bibliographic databases, narrowed down to 89 articles for review. Information related to TM techniques, the TM process, applications, challenges and recommendations for the improvement of TM results were extracted and synthesized. Eighteen TM techniques were identified and used to complete a variety of tasks such as data pre-processing, information retrieval, information extraction, text classification, clustering, topic modeling, text summarization and sentiment analysis or opinion mining. No single TM technique may be suitable for all text representation and extraction requirements. Therefore, context and applicability are key factors for TM technique selection.KeywordsText miningText mining techniquesUnstructured dataKnowledge discovery

Feature engineering is treated as a crucial step in any intelligent system pipeline and practitioners often spend 70–80% of their time in this phase before modeling. Especially, in machine learning (ML), the performance of the classification systems deteriorates as irrelevant features are added, even when the features presented contain enough information about the problem. ML researchers often use feature selection and feature extraction techniques individually to filter out the redundant information from the data, but would not get desired results or performance due to choosing a single method only. In this article, we take an attempt to combine both feature selection and feature extraction methods that give an improvement in the prediction performance than using a single feature selection or feature extraction method. In the application of the proposed method, we consider the Indian liver disease patient dataset to classify liver disease using machine learning classifiers. In addition, ensemble classifiers also take into account reproducing and robustifying classification accuracy. The performance is evaluated and compared with different metrics such as accuracy, precision, recall, and F1-score. Using the proposed method, the KNN classifier can diagnosis 76.03% with a precision of 76.69%, recall 96.23%, and 85.36% F1-score which is much better than the individual feature selection and feature extraction approach. The proposed method can also be extended to medical datasets other than liver disease so that these types of deadly diseases can be correctly diagnosed for the betterment of humanity.

Optical character recognition for printed Indian text is a classical problem because of round nature of these characters and presence of ldquomaatrasrdquo. Recognition accuracy of optical character recognition engines depends heavily upon correct representation of characters obtained by feature extraction method. Thus feature extraction is heart of recognition engines. We propose a novel spline approximation based method for feature extraction in recognition engines. The spline approximation is implemented for Indian characters and the results obtained are very encouraging as compared to existing feature extraction methods.

The increasing power of social media has created unprecedented opportunities for marketers. In particular, brand-owned social media seems to be an increasingly popular way of enhancing a brand’s position, connecting with customers, and improving customer engagement with the brand. To guide strategic marketing communication decision-making on social media, the current study extends the relationship communication model and offers an analytical workflow to gain new insights from unstructured textual data available on brand-owned social media. The workflow utilizes an eclectic mix of analytical tools such as word clouds, and cluster and word association analyses, which collectively allow for identification of main topics and their temporal evolution in unstructured textual data from a brand’s social media. In doing so, the proposed workflow offers researchers and practitioners a step by step procedure to make sense of such textual data, which may prove unwieldy and overwhelming otherwise. Furthermore, to manifest the utility of the proposed workflow, it is applied to illustrative data collected from a brand’s Facebook page. Results from this example analysis point to a slight fading of the brand’s perceived core position as an event avenue, as well as an evolution of customer sentiments that may reflect different levels and types of customer engagement with the page. Finally, we discuss the implications of our findings for research and brand management practice, as well as the study’s limitations and future research opportunities.

The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved.