Multilingual Sentiment Analysis Research Articles

Public Sentiment Analysis of the Israel-Palestine Conflict on Social Media Using BERT

General background Israel-Palestine conflict has drawn significant global attention, particularly in how it is perceived and discussed on social media platforms. Specific background understanding public sentiment surrounding such geopolitical issues is crucial for media monitoring, diplomatic efforts, and reputation management. Knowledge gap previous sentiment analysis studies often lack the ability to accurately handle multilingual and context-rich datasets, especially in analyzing neutral sentiments, which are commonly overlooked. This study aims to apply the Bidirectional Encoder Representations from Transformers (BERT) model to analyze public sentiment towards the Israel-Palestine conflict on the X platform, focusing on Indonesian users. Results Using BERT, the model achieved 93% accuracy, with a precision of 0.95, recall of 0.93, and F1-score of 0.94. The model performed well in predicting positive and negative sentiments but showed room for improvement in handling neutral sentiment. Novelty this study introduces the implementation of the BERT Transformer model for the multilingual and context-sensitive sentiment analysis of tweets, specifically addressing a high-stakes geopolitical conflict. Implications the findings demonstrate the potential for using advanced natural language processing techniques like BERT for monitoring public opinion, brand management, and detecting societal tensions on social media, offering valuable insights for stakeholders involved in conflict resolution and diplomatic strategies. Highlights: Achieved 93% accuracy in sentiment analysis using BERT on X platform. Identified strengths in predicting positive/negative sentiments, with challenges in neutral sentiment. Demonstrated BERT’s effectiveness in handling complex geopolitical social media data. Keywords: Sentiment Analysis, BERT Transformer, Israel-Palestine Conflict, Social Media, NLP

Digital technologies in linguistic education: Experience of development and implementation

The aim of this study was to share our experience of developing a digital Natural Language Processing Tool and its implementation in the process of training future linguists. In this article, we demonstrate the process of creating the web application SENTIALIZER, which is a multilingual Sentiment Analysis Tool developed with the help of the Python programming language and its libraries NLTK, BS4, TextBlob, Googletrans. The integration of Sentiment Analysis Tools into the educational framework is relied on the Unified Theory of Acceptance and Use of Technology (UTAUT) as its foundation. The results show that students see the prospects of using Sentiment Analysis Tools in their educational and professional activities, are ready to use them in the future, but are not ready to participate personally in projects to develop and improve such technologies. The reasons for this attitude are discussed. The presented study has a clear focus on student learning outcomes, which is an important criterion for the successful integration of technology into the educational process.

M2PSC: Multilingual sentiment analysis using improved multi-attention based Deep Learning model

Sentiment analysis, which involves determining the emotional polarity positivity, negativity, or neutrality in the source texts, is a crucial task. Multilingual sentiment analysis techniques were developed to analyze data in several languages; a notable deficiency of resources in multilingual sentiment analysis is one of the primary issues. Furthermore, the developed methods for multilingual sentiment analysis have some limitations such as data dependency, reliability, robustness, and computational complexity. To tackle these shortcomings, this research proposed a multilingual improved multi-attention Deep Learning model (M2PSC-DL), which leverages the advantages of the Bi-directional Long Short Term Memory (BiLSTM) classifier with improved attention mechanisms. The Multi-metric graph embedding technique encodes the data to provide more contextual information representation. Additionally, the combination of improved Positional Spatial Channel (SPC) attention increases the capability of the model to extract relevant features in the training process which leads to getting accurate results in sentiment analysis tasks. Furthermore, the research proposed an improved sigmoid activation for solving the vanishing gradient issues that help the model avoid gradient saturations. The validation results demonstrate that the M2PSC-DL model attains 96.26% accuracy, 96.06% precision, and 96.18% recall for the XED dataset which is far better than the traditional methods.

English Sentiment Analysis and its Application in Translation Based on Decision Tree Algorithm

Sentimental analysis belongs to the class of Natural Language Processing (NLP) based on the rule and machine model. The proposed model comprises of the pre-defined function for the estimation of the features in the English statements. This paper presents the Reflect Sentiment Translation Decision Tree (RSTDT), a novel model designed to integrate sentiment analysis and translation tasks for English text. The RSTDT model combines the strengths of decision tree algorithms with feature extraction techniques to accurately analyze sentiment and translate text across languages. The proposed RSTDT dataset comprises English sentences with annotated sentiment labels, the RSTDT model is trained to identify sentiment polarity and generate corresponding translations in Arabic. The proposed RSTDT model uses Traslation mapping for the estimation of the sentimental features. In order to estimate and classify the features in the neural network, the processes features are assessed using the decision tree model. The RSTDT model's efficacy in precisely capturing sentiment nuances and generating linguistically appropriate translations was shown through thorough testing and review. The model achieves high accuracy in sentiment analysis and exhibits proficiency in translating sentiment-rich content into Arabic while maintaining contextual relevance. Additionally, robust classification performance metrics underscore the model's efficacy in accurately classifying English words into sentiment categories. The RSTDT model offers a promising solution for multilingual sentiment analysis applications, with potential applications in social media monitoring, customer feedback analysis, and cross-cultural sentiment analysis.

Review on Multi-lingual Sentiment Analysis in Health Care

Multilingual sentiment analysis in healthcare is rapidly expanding, utilizing machine learning methods to identify emotions and sentiments in material written in several languages. This multidisciplinary field integrates computational linguistics, natural language processing, and health informatics to help healthcare providers better comprehend patient attitudes. Sentiment analysis is particularly useful in the healthcare industry since it facilitates comprehension of patient feedback, responses to interventions, and general contentment. Moreover, in an increasingly interconnected society, it can assist in recognizing the emotional states and concerns of patients from diverse linguistic origins. Healthcare providers can improve care and service by gaining insights into patient experiences through the analysis of patient reviews, social media posts, and other kinds of feedback in several languages. Sentiment analysis, for example, can be used to track patients' mental health over time and identify symptoms of depression or anxiety based on their interactions. These applications are becoming increasingly important for adjusting patient support and care, fostering better patient-provider communication, and eventually improving health outcomes. There are difficulties when implementing sentiment analysis in a multilingual setting, such as the requirement for extensive datasets in several languages and models that are sensitive to cultural quirks and context. By offering a foundation for creating more precise and sophisticated sentiment analysis systems that can function in a variety of linguistic and cultural contexts, advances in AI models, such as BERT and GPT variations, are assisting in addressing these issues. Recall that although sentiment analysis holds great potential, its use in healthcare needs to be done carefully to protect patient privacy and take ethical considerations into account. Sentiment analysis in healthcare can also assist in identifying unfulfilled medical and emotional demands of long-term patients, supporting patient-centred care models. In general, the incorporation of multilingual sentiment analysis into healthcare presents a multitude of opportunities and represents a promising facet of artificial intelligence's potential to enhance patient care outcomes and experiences.

SENTIMENT ANALYSIS OF INDONESIAN COMMUNITY TOWARDS ELECTRIC MOTORCYCLES ON TWITTER USING ORANGE DATA MINING

This study explores sentiment analysis of the Indonesian community towards electric motorcycles on Twitter using Orange Data Mining. In the context of the increasing popularity of electric vehicles, especially electric motorcycles, understanding public sentiment becomes crucial for various stakeholders. Twitter, as a leading social media platform, serves as a rich source of opinions and discussions on various topics, including electric motorcycles. This research utilizes Orange Data Mining with multilingual sentiment analysis techniques to analyze the sentiment of the Indonesian community regarding electric motorcycles. The results of sentiment analysis are visualized through box plots and scatter plots, aiming to classify Twitter users based on their emotional responses. The findings of this study provide valuable insights into the sentiment landscape surrounding electric motorcycles in Indonesia, benefiting policymakers, manufacturers, and marketers in understanding public perception and making informed decisions.

Knowledge-based Data Processing for Multilingual Natural Language Analysis

Natural Language Processing (NLP) aids the empowerment of intelligent machines by enhancing human language understanding for linguistic-based human-computer communication. Recent developments in processing power, as well as the availability of large volumes of linguistic data, have enhanced the demand for data-driven methods for automatic semantic analysis. This paper proposes multilingual data processing using feature extraction with classification using deep learning architectures. Here, the input text data has been collected based on various languages and processed to remove missing values and null values. The processed data has been extracted using Histogram Equalization based Global Local Entropy (HEGLE) and classified using Kernel-based Radial basis Function (Ker_Rad_BF). These architectures could be utilized to process natural language. We present solutions to the multilingual sentiment analysis issue in this research article by implementing algorithms, and we compare precision factors to discover the optimum option for multilingual sentiment analysis. For the HASOC dataset, the proposed HEGLE_ Ker_Rad_BF achieved an accuracy of 98%, a precision of 97%, a recall of 90.5%, an f-1 score of 85%, RMSE of 55.6%, and a loss curve analysis attained 44%. For the TRAC dataset, the accuracy of 98%, the precision attained is 97%, the Recall is 91%, the F-1 score is 87%, and the RMSE of the proposed neural network is 55%.

Multilingual sentiment analysis during the pandemic using deep learning models

High mood swings and increased mortality with the COVID-19 outbreak, mood changes are a concern. COVID-19 is extremely contagious, and by December 20, 2021, the global death toll from COVID-19 exceeded 5.35 million. Under such circumstances, people changed their original way of life and isolated themselves from home to work online. Most people in this situation expressed their emotions through social media. In order to understand the changes of people's emotions during the pandemic, this research analyzed the emotions of people's messages posted on social media in different countries through the SenWave dataset by different deep learning models. The dataset contains 6 languages (English, Arabic, Spanish, French, Italian and Chinese) with more than 70,000 tweets, in this paper, only tweets collected from Twitter are concerned, which contains 11 labels. The dataset will be preprocessed and transferred into numerical vector, then TextCNN, BERT, and BERT-TextCNN models are implemented for the above problems. BERT model reached 0.69 and 0.53 on precision and recall respectively on Twitter dataset.

Heterogeneous text graph for comprehensive multilingual sentiment analysis: capturing short- and long-distance semantics.

Multilingual sentiment analysis (MSA) involves the task of comprehending people's opinions, sentiments, and emotions in multilingual written texts. This task has garnered considerable attention due to its importance in extracting insights for decision-making across diverse fields such as marketing, finance, and politics. Several studies have explored MSA using deep learning methods. Nonetheless, a majority of these studies depend on sequential-based approaches, which focus on capturing short-distance semantics within adjacent word sequences, but they overlook long-distance semantics, which can provide more profound insights for analysis. In this work, we propose an approach for multilingual sentiment analysis, namely MSA-GCN, leveraging a graph convolutional network to effectively capture both short- and long-distance semantics. MSA-GCN involves the comprehensive modeling of the multilingual sentiment analysis corpus through a unified heterogeneous text graph. Subsequently, a slightly deep graph convolutional network is employed to acquire predictive representations for all nodes by encouraging the transfer learning across languages. Extensive experiments are carried out on various language combinations using different benchmark datasets to assess the efficiency of the proposed approach. These datasets include Multilingual Amazon Reviews Corpus (MARC), Internet Movie Database (IMDB), Allociné, and Muchocine. The achieved results reveal that MSA-GCN significantly outperformed all baseline models in almost all datasets with a p-value < 0.05 based on student t-test. In addition, such approach shows prominent results in a variety of language combinations, revealing the robustness of the approach against language variation.

Are Emotions Conveyed Across Machine Translations? Establishing an Analytical Process for the Effectiveness of Multilingual Sentiment Analysis with Italian Text

&#x0D; &#x0D; &#x0D; Abstract Natural language processing (NLP) is being widely used globally for a variety of value-creation tasks ranging from chat-bots and machine translations to sentiment and topic analysis and multilingual large language models (LLMs). However, most of the advances are initially implemented within the English language framework, and it takes time and resources to develop comparable resources in other languages. The advances in machine translations have enabled the rapid and effective conversion of content in global languages into English and vice-versa. This creates potential opportunities to apply English language NLP methods and tools to other languages via machine translations. However, although this idea is powerful, it needs to be validated and processes and best practices need to be developed and kept updated. The present research is an effort to contribute to the development of best practices and an evaluation framework. We present a systematic and repeatable state-of-the-art process to evaluate the viability of applying English language sentiment analysis tools to Italian text by using multiple English language machine translation mechanisms such that it can be easily extended to other languages.&#x0D; &#x0D; &#x0D;

Application of Deep Learning-Based Natural Language Processing in Multilingual Sentiment Analysis

This study explores the application of deep learning-based natural language processing technologies in multilingual sentiment analysis. By examining the performance of deep learning models such as BERT and LSTM in multilingual settings, the research demonstrates the effectiveness of these models in cross-linguistic sentiment classification tasks. Despite progress, major challenges in multilingual sentiment analysis include language and cultural differences, insufficient complex context processing, and data imbalance. Future research directions include optimizing the models' contextual understanding abilities, leveraging multilingual data resources, exploring novel neural network architectures, and improving assessment metrics. With these measures, the accuracy and efficiency of multilingual sentiment analysis are expected to be significantly enhanced, further advancing the global application of natural language processing technologies.

Optimizing Machine Learning-based Sentiment Analysis Accuracy in Bilingual Sentences via Preprocessing Techniques

With the recent advances in Natural Language Processing (NLP) technologies, the ability to process, analyze, and understand sentiments expressed in user-generated reviews regarding the products and services they use is becoming more achievable. Despite the latest improvements in this field, little attention has been given to multilingual sentiment analysis. In this article, a framework is presented for sentiment analysis in Arabic and English using two datasets (ASTD, AJGT) along with their translations. Preprocessing techniques, including n-gram tokenization, Arabic-specific stop words removal, punctuation removal, removing repeating characters, parts of speech tagging, stemming, and lemmatization, are applied. Four machine learning classifiers, namely Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM), are employed. We highlight existing specialized research in sentiment analysis for Arabic and English, as well as the employed techniques in each. Furthermore, the impact of preprocessing on accuracy results for both Arabic and English languages is investigated through separate experiments for each step. Experimental results on the ASTD dataset demonstrate close performance across classifiers, with the SVM classifier achieving the highest accuracy of 70%. However, the accuracy varied when using the AJGT dataset, with the NB classifier yielding the best accuracy at approximately 87%. The experiments on the translated datasets from Arabic to English did not exhibit significant differences, although some features performed slightly better using the Arabic datasets.

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With the recent advances in Natural Language Processing (NLP) technologies, the ability to process, analyze, and understand sentiments expressed in user-generated reviews regarding the products and services they use is becoming more achievable. Despite the latest improvements in this field, little attention has been given to multilingual sentiment analysis. In this article, a framework is presented for sentiment analysis in Arabic and English using two datasets (ASTD, AJGT) along with their translations. Preprocessing techniques, including n-gram tokenization, Arabic-specific stop words removal, punctuation removal, removing repeating characters, parts of speech tagging, stemming, and lemmatization, are applied. Four machine learning classifiers, namely Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM), are employed. We highlight existing specialized research in sentiment analysis for Arabic and English, as well as the employed techniques in each. Furthermore, the impact of preprocessing on accuracy results for both Arabic and English languages is investigated through separate experiments for each step. Experimental results on the ASTD dataset demonstrate close performance across classifiers, with the SVM classifier achieving the highest accuracy of 70%. However, the accuracy varied when using the AJGT dataset, with the NB classifier yielding the best accuracy at approximately 87%. The experiments on the translated datasets from Arabic to English did not exhibit significant differences, although some features performed slightly better using the Arabic datasets

Challenges and Opportunities in Multilingual Sentiment Analysis: Beyond English

Challenges and Opportunities in Multilingual Sentiment Analysis: Beyond English

Twitter Sentiment Analysis on Chatgpt Tweets

Abstract: This study investigates multilingual sentiment analysis within tweets on ChatGPT, an AI conversational model, employing Support Vector Machines (SVM) and BERT, an advanced language model. It aims to detect and classify emotions, including emoji identification, embedded within diverse messages across multiple languages on Twitter. By leveraging SVM's text classification and BERT's contextual understanding in various languages, the research delves into preprocessing techniques and feature engineering for sentiment analysis, encompassing multilingual and emoji detection. Furthermore, it explores the fusion of traditional SVM methods with BERT's state-of-the-art model for multilingual sentiment analysis, emphasizing emotion and emoji detection in AI-generated content on multilingual social media platforms like Twitter. This research yields insights into the successful detection of multilingual sentiment nuances and emotions, including emoji identification. It offers implications for advancing multilingual sentiment analysis in natural language processing across diverse linguistic contexts.

Multilingual Code-Mixed Sentiment Analysis in Hate Speech

Sentiment analysis discovers the emotion expressed in a text. It helps in analyzing the product reviews, customer feedback and survey responses. Researchers have developed various algorithms for this purpose, however, they have majorly focused only on the sentiment analysis in English language. Although, few works are available for Hindi and multilingual sentiment analysis, however, these works are not efficient enough to perform sentiment analysis in code-mixed languages. To overcome the limitation of the existing works, this paper presents a multilingual code-mixed language model which identifies the sentiments of the hate speech dataset extracted from Twitter. As the hate speech dataset with sentiment labels are not available, we first collect the data from Twitter. After that we label the data using a transformer-based pretrained sentiment analysis model trained on a large corpus of tweets in multiple languages. We pass our collected data as test data to this model and predict the sentiment labels. Now, we train six different machine learning models to perform our own task i.e sentiment analysis for multilingual code-mixed hate speech dataset. The machine learning models perform well across multiple languages and also code-mixed languages. In future, it can be easily adapted to different classification tasks based on code-mixed languages. The results yield that hate speech invokes negative sentiment whereas non-hate speech reflects either positive or neutral sentiment.

LANGUAGE DETECTION USING MACHINE LEARNING

This paper presents an innovative Machine Learning (ML) model for language detection that combines the power of logistic regression with a multimodal approach. The proposed model is designed to handle three types of inputs: sequential text data, files, and image representations. The proposed model offers a versatile and accurate solution for identifying languages across diverse data modalities. The model architecture employs logistic regression to enhance interpretability and feature extraction from each input modality. Trained on a comprehensive multilingual dataset, the model exhibits robust performance, showcasing its applicability to real-world scenarios. The model’s ability to process text, files, and images makes it well-suited for applications in content filtering, cross-modal information retrieval, and multilingual sentiment analysis. This research contributes to the advancement of language detection models by offering a unified solution for handling diverse input types.

An exploration of the causal factors making an online course content popular & engaging

An education system consists of imparting knowledge, assessing the learners, collecting feedback, analysing it, and taking proper measures to improve it. All these measures ultimately improve learner satisfaction which gets reflected in the learner performance and makes the course popular. Due to the corona pandemic, educational institutes and universities have shifted their activities online. In such a scenario, it is challenging to identify a course's popularity and the factors that make it popular. In this work, the authors have performed aspect-based sentiment analysis of learner feedback to identify the essential aspects of creating a popular course. The traditional methods for analysing feedback for sentiment analysis require manual intervention and are tedious. The authors have proposed a framework that identifies learner reviews' aspect level sentiment polarity and selected the significant aspects impacting a course's popularity. Experimental work is conducted over a large-scale real-world education dataset containing around 110 K learner comments (referred to as Educational Dataset now onwards) collected from Coursera1 and learner data from academic institution MSIT2. Different word embeddings viz., FastText, Word2Vec (Word2Vector), GloVe (Global representation of Vectors), and user-created embeddings are used for CNN (Convolution Neural Network) and LSTM (Long Short Term Memory) models. These models are further compared with BERT (Bidirectional Encoder Representation from Transformers), EvoMSA (Evolutionary Multilingual Sentiment Analysis) and ELMo (Embeddings from Language Model) classifiers to find the method that works best for identifying the aspects that impact the popularity of a course. Based on the experimental results over the collected Dataset, BERT based model provides the best results with an accuracy of 94.27%

Sentiment Analysis of IDAHOBIT Celebrations using Naïve Bayes and Decision Tree Algorithms

The development of LGBTIQ in Indonesia reflects the shift in culture and the emergence of this phenomenon has attracted the attention of the Indonesian people. The use of NLP, ML, and statistics technology in tweet analysis can be used to identify sentiments contained in tweets. This study compares Naïve Bayes algorithm and Decision Tree in sentiment analysis classification, in which the multilingual sentiment analysis method is used in the labeling process of training data. Naïve Bayes results give the best classification with 100% accuracy, precision, and recall, and the number of positive sentiments is 385, negative sentiments are 3117, and neutral sentiments are 899. It looks that the negative class is the most superior compared to other classes. This proves that the Indonesian people have an unfavorable response to the IDAHOBIT celebration.

Improved multi-lingual sentiment analysis and recognition using deep learning

Speech emotion recognition (SER) is still a fresh in natural language processing domain since the accuracy is beyond targeted. Mainly due to real-time applications such as human–robot interaction, human behaviour evaluation and virtual reality rely heavily on SER. Moreover, cross-lingual SER plays a significant role in practical applications, especially when users of different cultural and linguistic backgrounds interact with the system. However, the existing conventional approaches of SER cannot be employed for real-world applications because it uses the same corpus for training and testing, which cannot be used for multi-lingual environments to detect or classify real emotions. In such a situation, the performance of SER is degraded. Therefore, the proposed work develops cross-lingual emotion recognition through Urdu, Italian, English and German. The features are extracted through the most employed audio feature known as MFCCs (Mel Frequency Cepstral Coefficients). Experimental results exhibited that the proposed deep learning model comes out with promising results on the URDU data set with 91.25% accuracy using random forest (RF) and XGBoost classifier.