Evaluation of Naïve Bayes and Support Vector Machines on Bangla Textual Movie Reviews

Banik, Nayan; Rahman, Md. Hasan Hafizur

doi:10.1109/icbslp.2018.8554497

Cited by 32 publications

(5 citation statements)

References 19 publications

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“…This highlighted machine learning's potential in analyzing Bangla ecommerce reviews. Whereas, Banik and Rahman (2018) introduced a Bangla movie review sentiment analysis system using 800 annotated social media reviews. (Hasan et al, 2023b) introduced a significant dataset of 33,605 manually annotated Bangla social media posts and examined how different language models perform in zero-and few-shot learning situations.…”

Section: Advancements Of Sentiment Analysis In Banglamentioning

confidence: 99%

nlpBDpatriots at BLP-2023 Task 2: A Transfer Learning Approach towards Bangla Sentiment Analysis

Goswami,

Raihan,

Puspo

et al. 2023

Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)

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In this paper, we discuss the nlpBDpatriots entry to the shared task on Sentiment Analysis of Bangla Social Media Posts organized at the first workshop on Bangla Language Processing (BLP) co-located with EMNLP. The main objective of this task is to identify the polarity of social media content using a Bangla dataset annotated with positive, neutral, and negative labels provided by the shared task organizers. Our best system for this task is a transfer learning approach with data augmentation which achieved a micro F1 score of 0.71. Our best system ranked 12 th among 30 teams that participated in the competition.

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Section: Advancements Of Sentiment Analysis In Banglamentioning

confidence: 99%

nlpBDpatriots at BLP-2023 Task 2: A Transfer Learning Approach towards Bangla Sentiment Analysis

Goswami,

Raihan,

Puspo

et al. 2023

Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)

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“…From a modeling perspective, the existing literature addresses the problem using both classical machine learning and deep learning algorithms. These include Naive Bayes, Support Vector Machine, Decision Tree, Maximum Entropy, and Random Forest (Rahman and Hossen, 2019;Banik and Rahman, 2018;Chowdhury et al, 2019;Islam et al, 2016). Moreover, recent studies have extensively employed deep learning models for Bangla sentiment classification (Hassan et al, 2016;Aziz Sharfuddin et al, 2018;Tripto and Ali, 2018;Ashik et al, 2019;Karim et al, 2020;Sazzed, 2021;Sharmin and Chakma, 2021).…”

Section: Related Workmentioning

confidence: 99%

BLP-2023 Task 2: Sentiment Analysis

Hasan,

Alam,

Anjum

et al. 2023

Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)

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We present an overview of the BLP Sentiment Shared Task, organized as part of the inaugural BLP 2023 workshop, co-located with EMNLP 2023. The task is defined as the detection of sentiment in a given piece of social media text. This task attracted interest from 71 participants, among whom 29 and 30 teams submitted systems during the development and evaluation phases, respectively. In total, participants submitted 597 runs. However, a total of 15 teams submitted system description papers. The range of approaches in the submitted systems spans from classical machine learning models, fine-tuning pre-trained models, to leveraging Large Language Model (LLMs) in zero-and few-shot settings. In this paper, we provide a detailed account of the task setup, including dataset development and evaluation setup. Additionally, we provide a brief overview of the systems submitted by the participants. All datasets and evaluation scripts from the shared task have been made publicly available for the research community, to foster further research in this domain. 1

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“…Banik and Rahman [8] conducted a comparison of machine learning algorithms on Bengali emotional text interpretation. They made use of two distinct datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

Bangla News Classification Using GloVe Vectorization, LSTM, and CNN

Chowdhury

Eumi

Sarkar

et al. 2021

Lecture Notes on Data Engineering and Communications Technologies

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Text mining has gained considerable popularity over the last few years. Since the awareness of mobile media, streaming media, print media, and many other outlets are now accessible to consumers. Due to the large availability of text in many respects, research experts registered many unstructured data and found various ways in the literature of converting this dispersed text into a given, organized volume. Compared to the short text, the emphasis on complete classification (complete news, big records, long text, etc.) is prevalent. We addressed in this paper the process of text classification, grading, and various methodologies for feature extraction in short texts, i.e., news classification based on their headlines. Existing classification is compared and their operating methodologies presented efficiently. This work serves the purpose of classifying different types of Bangla Newspaper articles into 10 specific categories. The classification task is performed on Bengali text from three renowned newspapers of Kolkata. We have used advanced data tokenization techniques and unsupervised 'GloVe' vectorization for better classification performance. We applied LSTM and CNN as our main feature extractors. Comparing with other models like binary SVM classifier, standard LSTM, BiLSTM, CNN, or ANN, this proposed work gives better accuracy of 87%.

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Evaluation of Naïve Bayes and Support Vector Machines on Bangla Textual Movie Reviews

Cited by 32 publications

References 19 publications

nlpBDpatriots at BLP-2023 Task 2: A Transfer Learning Approach towards Bangla Sentiment Analysis

nlpBDpatriots at BLP-2023 Task 2: A Transfer Learning Approach towards Bangla Sentiment Analysis

BLP-2023 Task 2: Sentiment Analysis

Bangla News Classification Using GloVe Vectorization, LSTM, and CNN

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