Sentiment Analysis on Bengali Facebook Comments To Predict Fan's Emotions Towards a Celebrity

Khan, Md. Serajus Salekin; Rafa, Sanjida Reza; Abir, Al Ekram Hossain; Das, Amit Kumar

doi:10.38032/jea.2021.03.001

Cited by 20 publications

(6 citation statements)

References 20 publications

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“…Challenges such as sarcasm detection and negation handling are highlighted, alongside future directions aimed at dataset enrichment and algorithm exploration [2,16,17].Bengali, the research focuses on machine learning algorithms and transformer models like multilingual BERT and XLM-RoBERTa, fine-tuned on Bengali datasets. Despite the challenges associated with low-resource languages, including the scarcity of training corpora, these studies showcase the potential of advanced NLP models in sentiment analysis for Bengali, setting new benchmarks and encouraging further exploration [11,13,17,19].Sentiment analysis in Dravidian languages, particularly code-mixed text involving Kannada, Tamil, and Malayalam, employs deep learning models such as BERT to tackle the intricacies of analyzing sentiments in multilingual and code-mixed contexts. The challenges highlighted include English-based phonetic typing and word-level mixing, underscoring the need for models capable of effectively processing code-mixed data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This method has been applied to tackle various challenges in sentiment analysis, including named entity recognition, sarcasm detection, negation handling, and aspect-based analysis. The development and application of these lexicons have proven crucial in languages where machine learning resources may be scarce, highlighting the importance of linguistic and lexical resources in sentiment analysis [ 2,6,8,12,16,17,18,19,21,25,26,27,28,29,31,32].The phenomenon of code-mixing, where multiple languages are integrated within a single text, presents significant challenges for traditional NLP systems. This is particularly relevant in the Indian context, where bilingual or trilingual code-mixing is common.…”

Section: Literature Reviewmentioning

confidence: 99%

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Exploring Sentiment Analysis in Indian Regional Languages: Methods, Challenges, and Future Directions

Nemade

2024

IJSREM

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Sentiment Analysis, pivotal in natural language processing, extends its reach beyond English to Indian regional languages like Hindi, Marathi, Kannada, Konkani, Bengali, Khandeshi, and Urdu. This paper presents a comprehensive survey of 32 research papers in this domain, examining methodologies, datasets, and techniques while emphasizing the significance of sentiment analysis in diverse linguistic contexts for enhancing customer relationship management functionalities. It underscores the necessity for future research and highlights the efficacy of machine learning techniques. By elucidating on computational challenges and outlining various sentiment analysis methods, this paper serves as a critical resource for researchers and practitioners, fostering advancements in sentiment analysis tailored to regional linguistic nuances. KEYWORDS Bag Of Words, Hindi, Kannada, RNN, Konkani, Malayalam, Marathi, Maximum Entropy, Naive Bayes, Sentiment Analysis, SVM, TF-IDF, Urdu.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Exploring Sentiment Analysis in Indian Regional Languages: Methods, Challenges, and Future Directions

Nemade

2024

IJSREM

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“…Other ma-chine learning techniques were also employed for comparison, including Random Forest, K-Nearest Neighbors, Naive Bayes, and neural networks. The paper achieved an accuracy of 62% for support vector machines (SVM) [20]. Their dataset was not fully labeled.…”

Section: Related Workmentioning

confidence: 99%

Detection of Banglish Slang in Social Media Comments Using a Hybrid Bidirectional Long Short-Term Memory (Bi-LSTM) Model

Khalaf,

Yasmin,

Jisan

et al. 2024

Preprint

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Banglish slang is a distinctive mashup of Bengali and English terms that is widely used in messaging applications, social media, and other casual contexts. It has grown to be an essential component of Bangladesh's linguistic landscape. The use of Banglish slang in social media comments has become more common among young people from Bangladesh in recent years. Yet, because traditional language processing methods are not appropriate for managing such linguistic complexity, automated text analysis systems find it difficult to handle this informal communication style. Our proposed model captures the unique components of Banglish slang, such as slang phrases, misspellings, and abbreviations, by combining character-level and word-level properties since it is the most common form of communication and improves user interaction and content moderation. We remove special characters from the dataset and transform all test results to lowercase. After then, the stop words are removed from the dataset. Furthermore, we do tokenization using a pad sequence prior to classification. Stack Bidirectional Long Short-Term Memory (Bi-LSTM) model, a neural network architecture used in Natural Language Processing (NLP), is a state-of-the-art model utilized in this research to classify slang in Banglish words. On the training set with an outstanding 99% accuracy and a strong 79.8% validation accuracy, the suggested model beats traditional approaches. The Mendeley dataset was used to analyze social media comments to detect instances of Banglish slang. To carry out the work, approximately five thousand comments are considered overall. The experimental findings show how well our recommended approach works to identify Banglish slang. This method can be useful in monitoring content and language learning, among other real-world applications.

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“…Here, real IMDB and Amazon data sets were employed to estimate the suggested method performance. Similarly many approaches related to multi class sentiment classification [23][24][25][26][27] has been done in the previous studies but all those methods require better performance in terms of accuracy and the summary of literature review is presented in table 1 as shown below.…”

Section: Cognitive-inspired Domain Adaptationmentioning

confidence: 99%

Novel heuristic-based hybrid ResNeXt with recurrent neural network to handle multi class classification of sentiment analysis

Revathi

Satish²

2023

Mach. Learn.: Sci. Technol.

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Present-day, interdisciplinary research is increasing in social network-related applications, and it is a daily routine activity in every human life. So, sentiment analysis based on opinion mining is the most sophisticated concept in the well-known social network environment. Different machine learning methods were implemented to extract different text label features in sentiment analysis, and all of those methods can detect whether a given text is positive or negative based on the text features. Analysis of sentiment has been suffering from inaccuracies while using machine learning and sentiment-based lexical methods dependent on domain-specific problems. Multi-class sentiment analysis is an expensive task where memory, label samples, and other parameters are insufficient. So, we propose and implement a Novel Hybrid model which is a combination of ResNeXt and Recurrent Neural Framework (NH-ResNeXt-RNF) to explore multi-class sentiment from textual features. This framework investigates the polarity of words connected to a specific domain across the entire dataset and eliminates noisy data in an unsupervised manner using pre-processing .Optimization is required to perform efficient multi-class classification to reduce the effort associated with annotation for multi-class sentiment analysis via unsupervised learning. The proposed model performance is evaluated on two data sets namely: Amazon and Twitter. We increase the accuracy of the sentiment of polarity on each sentence present in the data set. Experimental results of the proposed approach give better and more efficient multi-class (positive, negative, very positive, neutral and highly negative) domain-specific sentiment than traditional approaches related to supervised, semi-supervised, and unsupervised domains. The proposed hybrid model accuracy is 96.5% and 95.37% for Amazon and Twitter datasets respectively.

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Sentiment Analysis on Bengali Facebook Comments To Predict Fan's Emotions Towards a Celebrity

Cited by 20 publications

References 20 publications

Exploring Sentiment Analysis in Indian Regional Languages: Methods, Challenges, and Future Directions

Exploring Sentiment Analysis in Indian Regional Languages: Methods, Challenges, and Future Directions

Detection of Banglish Slang in Social Media Comments Using a Hybrid Bidirectional Long Short-Term Memory (Bi-LSTM) Model

Novel heuristic-based hybrid ResNeXt with recurrent neural network to handle multi class classification of sentiment analysis

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