fastText Word Embedding Research Articles

Enhancing Sentiment Analysis of Indonesian Tourism Video Content Commentary on TikTok: A FastText and Bi-LSTM Approach

Sentiment analysis is a method used to measure public opinion or the emotions of a group of people with similar interests based on their reactions to an event through text, images, videos, or audio on social media. However, such online data presents several challenges that can hinder the sentiment analysis process. These challenges stem mainly from the freedom that users have to post their content. Additionally, irrelevant opinions, often referred to as fake opinions, can also arise. The Bi-LSTM approach processes input sequences bidirectionally, allowing the model to capture information from both previous and subsequent contexts. This method is well-suited for sentiment analysis tasks due to its ability to recognize language nuances and relationships between different parts of the text. This study integrates a Bi-LSTM model with FastText word embeddings to filter out irrelevant opinions considered spam. The dataset consists of 150,351 TikTok comments taken from 100 popular videos related to tourist attractions. The experimental results show that the proposed Bi-LSTM model outperforms other models such as LSTM, CNN, GRU, MD-LSTM, and Peephole LSTM, achieving a test accuracy of 89.18%. Furthermore, when slang word translation is performed to convert slang into formal words, the Bi-LSTM model shows further improvement, with test accuracy reaching 93.10%, again surpassing the baseline models. These results demonstrate the robustness of the proposed method in handling noisy and informal language, thus improving the accuracy of sentiment analysis in the context of social media. This study provides a foundation for future research to improve sentiment analysis by addressing domain-specific challenges such as data imbalance and noise in social media data.

Deepfake Detection on Social Media: Leveraging Deep Learning and Fast Text Embeddings for Identifying Machine-Generated Tweets

Recent improvements in natural language generation offer a new means to influence public opinion on social media. Moreover, developments in language modelling have considerably augmented the generative capacities of deep neural models, equipping them with improved competencies for content creation. As a result, text-generative models have gained significant potency, enabling adversaries to leverage these capabilities to enhance social bots, facilitating the creation of authentic deepfake postings and swaying public conversation. In order to address this issue, trustworthy and efficient techniques for detecting social media deepfakes are essential. Automated text on Twitter has been identified in recent studies. To determine if tweets are generated by humans or bots, this study employs the publicly available Tweepfake dataset and a basic deep learning model that makes use of word embeddings. Using a CNN architecture, FastText word embeddings can detect deepfake tweets. This study utilised many machine learning models as baseline approaches to demonstrate the higher performance of the proposed approach. Some of the fundamental techniques used here were Term Frequency, Fast Text subword embeddings, FastText, and Term Frequency-Inverse Document Frequency. Additional comparisons with other DL models, such as CNN-LSTM and LSTM, demonstrate the effectiveness and benefits of the suggested approach in precisely resolving the issue. The experimental results demonstrate that Twitter data may be efficiently and accurately classified with a 93% accuracy rate using CNN architecture and FastText embeddings.

CONTENT-BASED FILTERING CULINARY RECOMMENDATION SYSTEM USING DEEP CONVOLUTIONAL NEURAL NETWORK ON TWITTER (X)

Along with the development of technology, social media has become integral to everyday life, especially for sharing content like culinary reviews. Social media platform X (formerly Twitter) is often used for sharing culinary recommendations, but the abundance of information makes it difficult for users to find relevant suggestions. In order to improve rating prediction performance, this study suggests a recommendation system model that is more thoroughly created utilizing Content-Based Filtering (CBF) combined with Deep Convolutional Neural Network (CNN) and optimised with Particle Swarm Optimization (PSO). Data was collected from PergiKuliner and Twitter, totaling 2644 reviews and 200 cuisines. The preprocessing involved text processing, translation, and polarity assessment. Post-labeling, 7438 data were labeled with 0 and 1562 with 1. Label 0 means not recommended while label 1 means recommended. The imbalance is handled by applying the SMOTE method after observing that the fraction of data labeled 0 and 1 is 65.2%. CBF employed TF-IDF feature extraction and FastText word embedding, while Deep CNN handled classification. PSO optimisation was applied to enhance the accuracy of culinary rating predictions. The results showed an initial accuracy of 76.32% with the baseline Deep CNN model, which increased to 86.06% after Nadam optimisation with the best learning rate, and further reached 86.18% after PSO optimisation on dense units. The 9.86% accuracy improvement from the baseline model demonstrates the effectiveness of the combined methods.

Enhancing misogyny detection in bilingual texts using explainable AI and multilingual fine-tuned transformers

Gendered disinformation undermines women’s rights, democratic principles, and national security by worsening societal divisions through authoritarian regimes’ intentional weaponization of social media. Online misogyny represents a harmful societal issue, threatening to transform digital platforms into environments that are hostile and inhospitable to women. Despite the severity of this issue, efforts to persuade digital platforms to strengthen their protections against gendered disinformation are frequently ignored, highlighting the difficult task of countering online misogyny in the face of commercial interests. This growing concern underscores the need for effective measures to create safer online spaces, where respect and equality prevail, ensuring that women can participate fully and freely without the fear of harassment or discrimination. This study addresses the challenge of detecting misogynous content in bilingual (English and Italian) online communications. Utilizing FastText word embeddings and explainable artificial intelligence techniques, we introduce a model that enhances both the interpretability and accuracy in detecting misogynistic language. To conduct an in-depth analysis, we implemented a range of experiments encompassing classic machine learning methodologies and conventional deep learning approaches to the recent transformer-based models incorporating both language-specific and multilingual capabilities. This paper enhances the methodologies for detecting misogyny by incorporating incremental learning for cutting-edge datasets containing tweets and posts from different sources like Facebook, Twitter, and Reddit, with our proposed approach outperforming these datasets in metrics such as accuracy, F1-score, precision, and recall. This process involved refining hyperparameters, employing optimization techniques, and utilizing generative configurations. By implementing Local Interpretable Model-agnostic Explanations (LIME), we further elucidate the rationale behind the model’s predictions, enhancing understanding of its decision-making process.

Ensemble based high performance deep learning models for fake news detection

Social media has emerged as a dominant platform where individuals freely share opinions and communicate globally. Its role in disseminating news worldwide is significant due to its easy accessibility. However, the increase in the use of these platforms presents severe risks for potentially misleading people. Our research aims to investigate different techniques within machine learning, deep learning, and ensemble learning frameworks in Arabic fake news detection. We integrated FastText word embeddings with various machine learning and deep learning methods. We then leveraged advanced transformer-based models, including BERT, XLNet, and RoBERTa, optimizing their performance through careful hyperparameter tuning. The research methodology involves utilizing two Arabic news article datasets, AFND and ARABICFAKETWEETS datasets, categorized into fake and real subsets and applying comprehensive preprocessing techniques to the text data. Four hybrid deep learning models are presented: CNN-LSTM, RNN-CNN, RNN-LSTM, and Bi-GRU-Bi-LSTM. The Bi-GRU-Bi-LSTM model demonstrated superior performance regarding the F1 score, accuracy, and loss metrics. The precision, recall, F1 score, and accuracy of the hybrid Bi-GRU-Bi-LSTM model on the AFND Dataset are 0.97, 0.97, 0.98, and 0.98, and on the ARABICFAKETWEETS dataset are 0.98, 0.98, 0.99, and 0.99 respectively. The study’s primary conclusion is that when spotting fake news in Arabic, the Bi-GRU-Bi-LSTM model outperforms other models by a significant margin. It significantly aids the global fight against false information by setting the stage for future research to expand fake news detection to multiple languages.

Enhanced automated text categorization via Aquila optimizer with deep learning for Arabic news articles

Text Classification is the traditional Natural Language Processing (NLP) task. Text classification (also known as categorization) has become a cutting-edge research area in recent years. However, this task has received less attention in Arabic due to the need for more extensive resources for training Arabic text classifiers. In the area of text classification for Arabic news articles, deep learning (DL) methods, namely recurrent neural network (RNN) and convolutional neural network (CNN), were effectively used. This model is trained on labelled datasets around many news topics to automatically categorize articles into predetermined classes. These DL techniques can efficiently discern the subject matter by leveraging the contextual and semantic data embedded in the Arabic text, enabling accurate classification. This application of DL facilitates effective retrieval and organization of Arabic news articles, which supports tasks such as personalized content recommendations, information retrieval, and summarization. Therefore, this study presents an Enhanced Automated Text Categorization via Aquila Optimizer with Deep Learning for Arabic News Articles (TCAODL-ANA) technique. The TCAODL-ANA technique aims to detect and classify Arabic news articles into seven classes. The TCAODL-ANA technique follows pre-processing and the FastText word embedding process to accomplish this. In addition, the TCAODL-ANA technique utilizes an effective attention-based bidirectional gated recurrent unit (ABiGRU) method to identify various news articles. To enhance the detection results of the ABiGRU method, the AO model is employed for the hyperparameter selection process. A comprehensive simulation evaluation is performed to emphasize the improved performance of the TCAODL-ANA technique. The investigational validation portrayed the superior outcomes of the TCAODL-ANA technique over existing techniques.

Service similarity measurement integrating Bi-LSTM contextual representation and attention mechanism for web service discovery

With the rapid growth and wide adoption of web services, service discovery is becoming commonly used to locate the optimal services that can meet the requirements of users. This paper proposes a bidirectional long short-term memory (Bi-LSTM) service discovery method that integrates contextual representation and attention mechanisms for web service discovery. To conduct the study, FastText word embedding with the N-gram feature is primarily used as the input of the Bi-LSTM model to obtain the context feature vector of the service function description. Moreover, the attention mechanism is employed to calculate the similarity of the service function description to gain the semantic feature vector of the correlation degree between services. Then, the multilayer perceptron network is used to mine the mapping relationship between semantic feature vectors and matching levels among services. Finally, for a given query, target services are retrieved by ranking candidate services according to the prediction cores through the matching method. The proposed method is examined against multiple evaluation metrics, including accuracy and error, and compared with the state-of-the-art searching approaches. The experimental results show that the proposed method is more effective and attains better relevant web services, which helps the request service find more accurate results. Furthermore, this study provides new insights for researchers into web service discovery.

Comparison of Deep Learning and Machine Learning Model for Phishing Email Classification

Email is a medium in business communication that people use everyday and not only holds sensitive information but also identity for the user and organization. The uniqueness of information contained make phishing detection and remedy cannot be applied generally. Existing method applied in common on security software such as blacklisting keyword or email address is not enough to outpace evolving phishing method. Nowadays the implementation of machine learning and deep learning grow rapidly fast and the method used by machine learning and deep learning that can learn pattern of inputs and natural language processing making classification to detect email phishing promising, this study present proposed method of phishing mail classification by comparing the use of machine learning and deep learning model, The algorithm used in this paper are recurrent convolutional neural network and random forest with tf-idf and fasttext word embedding. The Dataset contain phishing and legitimate of an email gathered from a trading company in Indonesia. The dataset will be split into 70% for training and 30% for testing. As a result of this comparison, the random forest model with tf-idf word embedding achieve highest accuracy of 100% for the dataset used and the highest accuracy for recurrent convolutional neural network model with fasttext is 98.21%.

Combining Multiple Text Representations for Improved Automatic Evaluation of Indonesian Essay Answers

multiple-choices, regarding students’ learning achievement. When the number of students in a class is huge, however, examinations using essay questions become hard to conduct and take a long evaluation time. Automatic essay evaluation has, therefore, become a potential approach in this situation. Various methods have been proposed, however, optimal solutions for such evaluation in the Indonesian language are less known. Furthermore, with the rapid development of machine learning approaches, in particular deep learning approaches, the investigation of such optimal solutions becomes more necessary. Method: To address the aforementioned issue, this study proposed the investigation of text representation approaches for optimal automatic evaluation of Indonesian essay answers. The investigation compared pre-trained word embedding methods such as Word2vec, GloVe, FastText, and RoBERTa, as well as compared text encoding methods such as long short-term memories (LSTMs) and transformers. LSTMs are able to capture temporal semantics by employing state variables, while transformers are able to capture long-term dependency between parts of their input sequences. Additionally, we investigated classification-based and similarity-based training to build text encoders. We expected that these training approaches allowed encoders to extract different views of information. We compared classification results produced by different text encoders and combinations of text encoders. Result: We evaluated various text representation approaches using the UKARA dataset. Our experiments showed that the FastText word embedding method outperformed the Word2vec, GloVe, and RoBERTa methods. The FastText method achieved an F1-score of 75.43% on validation sets, while the Word2vec, GloVe, and RoBERTa methods achieved F1-scores of 69.56%, 74.53%, and 72.87%, respectively. In addition, the experiments showed that combinations of text encoders outperformed individual encoders. The combination of the LSTM encoder, the transformer encoder, and the TF-IDF encoder obtained an F1-score of 77.22% in the best case, which is better than the best F1-scores of the individual LSTM encoders (75.35%), the best combination of transformer encoders (71.49%), and the individual TF-IDF encoder (76.69%). We observed that LSTM encoders produced better performance when they were built using classification-based training. Meanwhile, the transformer encoders obtained better performance when built using similarity-based training. Novelty: The novelty proposed in this research is the optimal combination of text encoders specifically constructed for the evaluation of essay answers in the Indonesian language. Our experiments showed that the combination of three encoders - namely the LSTM encoder built using classification-based training, the transformer encoder built using classification-based and similarity-based training, and the TF-IDF encoder - obtained the best classification performance.

Abusive Language Detection in Khasi Social Media Comments

Abusive Language Detection in Khasi Social Media Comments

Analyzing Reddit Data: Hybrid Model for Depression Sentiment using FastText Embedding

Depression, a prevalent mental condition globally, exerts a substantial influence on various aspects of human cognition, emotions, and behaviour. The alarming increase in deaths attributable to depression over the past few years demonstrates the imperative need to address this problem through prevention and treatment interventions. In the era of thriving social media platforms, which have a significant impact on society and psychological aspects, these platforms have become a means for people to convey their emotions and experiences openly. Reddit stands out among these platforms as a significant place. The main aim of this study is to examine the feasibility of forecasting individuals' mental states by classifying Reddit articles on depression and non-depression. This work aims to employ deep learning algorithms and word embeddings to analyze the textual and semantic settings of narratives to detect symptoms of depression. The study effectively employed a BiLSTM-BiGRU model that applied FastText word embeddings. The BiLSTM-BiGRU model analyzes information bidirectionally, detecting correlations in sequential data. It's suitable for tasks dependent on input order or addressing data uncertainties. The Reddit dataset, featuring text concerning depression, achieved an accuracy score of 97.03% and an F1 score of 97.02%.

A deep learning approach for Named Entity Recognition in Urdu language.

Named Entity Recognition (NER) is a natural language processing task that has been widely explored for different languages in the recent decade but is still an under-researched area for the Urdu language due to its rich morphology and language complexities. Existing state-of-the-art studies on Urdu NER use various deep-learning approaches through automatic feature selection using word embeddings. This paper presents a deep learning approach for Urdu NER that harnesses FastText and Floret word embeddings to capture the contextual information of words by considering the surrounding context of words for improved feature extraction. The pre-trained FastText and Floret word embeddings are publicly available for Urdu language which are utilized to generate feature vectors of four benchmark Urdu language datasets. These features are then used as input to train various combinations of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), CRF, and deep learning models. The results show that our proposed approach significantly outperforms existing state-of-the-art studies on Urdu NER, achieving an F-score of up to 0.98 when using BiLSTM+GRU with Floret embeddings. Error analysis shows a low classification error rate ranging from 1.24% to 3.63% across various datasets showing the robustness of the proposed approach. The performance comparison shows that the proposed approach significantly outperforms similar existing studies.

Multi-class hate speech detection in the Norwegian language using FAST-RNN and multilingual fine-tuned transformers

The growth of social networks has provided a platform for individuals with prejudiced views, allowing them to spread hate speech and target others based on their gender, ethnicity, religion, or sexual orientation. While positive interactions within diverse communities can considerably enhance confidence, it is critical to recognize that negative comments can hurt people’s reputations and well-being. This emergence emphasizes the need for more diligent monitoring and robust policies on these platforms to protect individuals from such discriminatory and harmful behavior. Hate speech is often characterized as an intentional act of aggression directed at a specific group, typically meant to harm or marginalize them based on certain aspects of their identity. Most of the research related to hate speech has been conducted in resource-aware languages like English, Spanish, and French. However, low-resource European languages, such as Irish, Norwegian, Portuguese, Polish, Slovak, and many South Asian, present challenges due to limited linguistic resources, making information extraction labor-intensive. In this study, we present deep neural networks with FastText word embeddings using regularization methods for multi-class hate speech detection in the Norwegian language, along with the implementation of multilingual transformer-based models with hyperparameter tuning and generative configuration. FastText outperformed other deep learning models when stacked with Bidirectional LSTM and GRU, resulting in the FAST-RNN model. In the concluding phase, we compare our results with the state-of-the-art and perform interpretability modeling using Local Interpretable Model-Agnostic Explanations to achieve a more comprehensive understanding of the model’s decision-making mechanisms.

Construction and analysis of uncertainty indices based on multilingual text representations

The work by Baker et al. (2016), who propose a dictionary based method and estimate the level of economic policy uncertainty (EPU) based on the occurrence of specific terms in ten leading newspapers in the USA, is among the first ones to detect the potential of text data in economic research. Following this line of research, this paper proposes automated approaches to construction of EPU indices for different countries based on newspapers’ texts. Multilingual fastText word embeddings, (S)BERT embeddings, and a novel multilingual topic modeling approach are used to construct EPU indices for Germany, Russia, and Ukraine. It is shown that constructed EPU indices based on multilingual word embeddings are Granger causal to the economic activity in all of the considered countries.

Automatic de-identification of French electronic health records: a cost-effective approach exploiting distant supervision and deep learning models

BackgroundElectronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. De-identification is therefore essential to protect personal data in EHRs and comply with government regulations. Named entity recognition (NER) methods have been proposed to remove personal identifiers, with deep learning-based models achieving better performance. However, manual annotation of training data is time-consuming and expensive. The aim of this study was to develop an automatic de-identification pipeline for all kinds of clinical documents based on a distant supervised method to significantly reduce the cost of manual annotations and to facilitate the transfer of the de-identification pipeline to other clinical centers.MethodsWe proposed an automated annotation process for French clinical de-identification, exploiting data from the eHOP clinical data warehouse (CDW) of the CHU de Rennes and national knowledge bases, as well as other features. In addition, this paper proposes an assisted data annotation solution using the Prodigy annotation tool. This approach aims to reduce the cost required to create a reference corpus for the evaluation of state-of-the-art NER models. Finally, we evaluated and compared the effectiveness of different NER methods.ResultsA French de-identification dataset was developed in this work, based on EHRs provided by the eHOP CDW at Rennes University Hospital, France. The dataset was rich in terms of personal information, and the distribution of entities was quite similar in the training and test datasets. We evaluated a Bi-LSTM + CRF sequence labeling architecture, combined with Flair + FastText word embeddings, on a test set of manually annotated clinical reports. The model outperformed the other tested models with a significant F1 score of 96,96%, demonstrating the effectiveness of our automatic approach for deidentifying sensitive information.ConclusionsThis study provides an automatic de-identification pipeline for clinical notes, which can facilitate the reuse of EHRs for secondary purposes such as clinical research. Our study highlights the importance of using advanced NLP techniques for effective de-identification, as well as the need for innovative solutions such as distant supervision to overcome the challenge of limited annotated data in the medical domain.

AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria.

Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify ABPs and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict ABPs and obtained high precision with low sensitivity. To address the issue of poor sensitivity, we developed alignment-free methods for predicting ABPs using machine/deep learning techniques. In the case of alignment-free methods, we utilized a wide range of peptide features that include different types of composition, binary profiles of terminal residues, and fastText word embedding. In this study, a five-fold cross-validation technique has been used to build machine/deep learning models on training datasets. These models were evaluated on an independent dataset with no common peptide between training and independent datasets. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98, and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, on an independent dataset. Our method performs better than existing methods when compared with existing approaches on an independent dataset. A user-friendly web server, standalone package and pip package have been developed to facilitate peptide-based therapeutics.

Political Optimization Algorithm with a Hybrid Deep Learning Assisted Malicious URL Detection Model

With the enhancement of the Internet of Things (IoT), smart cities have developed the idea of conventional urbanization. IoT networks permit distributed smart devices to collect and process data in smart city structures utilizing an open channel, the Internet. Accordingly, challenges like security, centralization, privacy (i.e., execution data poisoning and inference attacks), scalability, transparency, and verifiability restrict faster variations of smart cities. Detecting malicious URLs in an IoT environment is crucial to protect devices and the network from potential security threats. Malicious URL detection is an essential element of cybersecurity. It is established that malicious URL attacks mean large risks in smart cities, comprising financial damages, losses of personal identifications, online banking, losing data, and loss of user confidentiality in online businesses, namely e-commerce and employment of social media. Therefore, this paper concentrates on the proposal of a Political Optimization Algorithm by a Hybrid Deep Learning Assisted Malicious URL Detection and Classification for Cybersecurity (POAHDL-MDC) technique. The presented POAHDL-MDC technique identifies whether malicious URLs occur. To accomplish this, the POAHDL-MDC technique performs pre-processing to transform the data to a compatible format, and a Fast Text word embedding process is involved. For malicious URL recognition, a Hybrid Deep Learning (HDL) model integrates the features of stacked autoencoder (SAE) and bi-directional long short-term memory (Bi-LSTM). Finally, POA is exploited for optimum hyperparameter tuning of the HDL technique. The simulation values of the POAHDL-MDC approach are tested on a Malicious URL database, and the outcome exhibits an improvement of the POAHDL-MDC technique with a maximal accuracy of 99.31%.

AIPs-SnTCN: Predicting Anti-Inflammatory Peptides Using fastText and Transformer Encoder-Based Hybrid Word Embedding with Self-Normalized Temporal Convolutional Networks.

Inflammation is a biologically resistant response to harmful stimuli, such as infection, damaged cells, toxic chemicals, or tissue injuries. Its purpose is to eradicate pathogenic micro-organisms or irritants and facilitate tissue repair. Prolonged inflammation can result in chronic inflammatory diseases. However, wet-laboratory-based treatments are costly and time-consuming and may have adverse side effects on normal cells. In the past decade, peptide therapeutics have gained significant attention due to their high specificity in targeting affected cells without affecting healthy cells. Motivated by the significance of peptide-based therapies, we developed a highly discriminative prediction model called AIPs-SnTCN to predict anti-inflammatory peptides accurately. The peptide samples are encoded using word embedding techniques such as skip-gram and attention-based bidirectional encoder representation using a transformer (BERT). The conjoint triad feature (CTF) also collects structure-based cluster profile features. The fused vector of word embedding and sequential features is formed to compensate for the limitations of single encoding methods. Support vector machine-based recursive feature elimination (SVM-RFE) is applied to choose the ranking-based optimal space. The optimized feature space is trained by using an improved self-normalized temporal convolutional network (SnTCN). The AIPs-SnTCN model achieved a predictive accuracy of 95.86% and an AUC of 0.97 by using training samples. In the case of the alternate training data set, our model obtained an accuracy of 92.04% and an AUC of 0.96. The proposed AIPs-SnTCN model outperformed existing models with an ∼19% higher accuracy and an ∼14% higher AUC value. The reliability and efficacy of our AIPs-SnTCN model make it a valuable tool for scientists and may play a beneficial role in pharmaceutical design and research academia.

Low-Resource Language Information Processing using Dwarf Mongoose Optimization with Deep Learning Based Sentiment Classification

Asian and low-resource language information processing refers to the field of computational linguistics that aims to develop natural language processing (NLP) technologies for languages that have fewer available language resources or are less commonly spoken. This is an important field of study because many languages in Asia and other parts of the world are underrepresented in the field of NLP, which may limit access to information and technology for speakers of these languages. The growing volume of user-generated content on the web has made sentiment analysis (SA) a significant tool for extracting data regarding human emotional states. Twitter sentiment detectors provide a superior solution for assessing the quality of products and services compared to other conventional technologies. The detection performance and classifier accuracy of SA, which can be highly dependent on classifier methods and the quality of input features have been utilised. Deep learning (DL) methods use distinct techniques to extract data from raw data such as tweets or texts and represent them in different forms of models. Therefore, this article presents a Dwarf Mongoose Optimization with Deep Learning-Based Twitter Sentiment Classification (DMODL-TSC) technique to classify sentiments based on tweets. The presented DMODL-TSC technique leverages the concepts of natural language processing (NLP) and DL. Primarily, the raw tweets are preprocessed to transform them into a useful format. Next, the DMODL-TSC technique uses the advanced FastText word embedding technique. Moreover, the bidirectional recurrent neural network (BiRNN) method is utilized for the recognition of sentiments. Finally, the DMO technique is utilized for the optimal hyperparameter optimization of the BiRNN method, which leads to effective classification performance. The comprehensive result examination of the DMODL-TSC system was tested on three datasets, and the obtained outcomes illustrate the supremacy of the DMODL-TSC approach.

Hate Speech Detection in Indonesia Twitter Comments Using Convolutional Neural Network (CNN) and FastText Word Embedding

Hate Speech Detection in Indonesia Twitter Comments Using Convolutional Neural Network (CNN) and FastText Word Embedding