Analyzing Emotions in Bangla Social Media Comments Using Machine Learning and LIME

• URL: http://arxiv.org/abs/2506.10154v1
• Date: Wed, 11 Jun 2025 20:15:42 GMT
• Title: Analyzing Emotions in Bangla Social Media Comments Using Machine Learning and LIME
• Authors: Bidyarthi Paul, SM Musfiqur Rahman, Dipta Biswas, Md. Ziaul Hasan, Md. Zahid Hossain,
• Abstract summary: This study examines emotion analysis using 22,698 social media comments from the EmoNoBa dataset.<n>We employ machine learning models: Linear SVM, KNN, and Random Forest with n-gram data from a TF-IDF vectorizer.<n>We additionally investigated how PCA affects the reduction of dimensionality.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Research on understanding emotions in written language continues to expand, especially for understudied languages with distinctive regional expressions and cultural features, such as Bangla. This study examines emotion analysis using 22,698 social media comments from the EmoNoBa dataset. For language analysis, we employ machine learning models: Linear SVM, KNN, and Random Forest with n-gram data from a TF-IDF vectorizer. We additionally investigated how PCA affects the reduction of dimensionality. Moreover, we utilized a BiLSTM model and AdaBoost to improve decision trees. To make our machine learning models easier to understand, we used LIME to explain the predictions of the AdaBoost classifier, which uses decision trees. With the goal of advancing sentiment analysis in languages with limited resources, our work examines various techniques to find efficient techniques for emotion identification in Bangla.

Related papers

• PromotionGo at SemEval-2025 Task 11: A Feature-Centric Framework for Cross-Lingual Multi-Emotion Detection in Short Texts [1.210852962855694]
  This paper presents our system for SemEval 2025 Task 11: Bridging the Gap in Text-Based Emotion Detection.<n>We propose a feature-centric framework that dynamically adapts document representations and learning algorithms to optimize language-specific performance.
  arXiv  Detail & Related papers  (2025-07-11T11:21:18Z)
• EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian [60.61343989805093]
  EmoBench-UA is the first annotated dataset for emotion detection in Ukrainian texts.<n>Our findings highlight the challenges of emotion classification in non-mainstream languages like Ukrainian.
  arXiv  Detail & Related papers  (2025-05-29T09:49:57Z)
• BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages [93.92804151830744]
  We present BRIGHTER, a collection of multi-labeled, emotion-annotated datasets in 28 different languages.<n>We highlight the challenges related to the data collection and annotation processes.<n>We show that the BRIGHTER datasets represent a meaningful step towards addressing the gap in text-based emotion recognition.
  arXiv  Detail & Related papers  (2025-02-17T15:39:50Z)
• Holmes: A Benchmark to Assess the Linguistic Competence of Language Models [59.627729608055006]
  We introduce Holmes, a new benchmark designed to assess language models (LMs) linguistic competence. We use computation-based probing to examine LMs' internal representations regarding distinct linguistic phenomena. As a result, we meet recent calls to disentangle LMs' linguistic competence from other cognitive abilities.
  arXiv  Detail & Related papers  (2024-04-29T17:58:36Z)
• Exploring Tokenization Strategies and Vocabulary Sizes for Enhanced Arabic Language Models [0.0]
  This paper examines the impact of tokenization strategies and vocabulary sizes on the performance of Arabic language models. Our study uncovers limited impacts of vocabulary size on model performance while keeping the model size unchanged. Paper's recommendations include refining tokenization strategies to address dialect challenges, enhancing model robustness across diverse linguistic contexts, and expanding datasets to encompass the rich dialect based Arabic.
  arXiv  Detail & Related papers  (2024-03-17T07:44:44Z)
• Arabic Sentiment Analysis with Noisy Deep Explainable Model [48.22321420680046]
  This paper proposes an explainable sentiment classification framework for the Arabic language. The proposed framework can explain specific predictions by training a local surrogate explainable model. We carried out experiments on public benchmark Arabic SA datasets.
  arXiv  Detail & Related papers  (2023-09-24T19:26:53Z)
• Analysis of the Evolution of Advanced Transformer-Based Language Models: Experiments on Opinion Mining [0.5735035463793008]
  This paper studies the behaviour of the cutting-edge Transformer-based language models on opinion mining. Our comparative study shows leads and paves the way for production engineers regarding the approach to focus on.
  arXiv  Detail & Related papers  (2023-08-07T01:10:50Z)
• REDAffectiveLM: Leveraging Affect Enriched Embedding and Transformer-based Neural Language Model for Readers' Emotion Detection [3.6678641723285446]
  We propose a novel approach for Readers' Emotion Detection from short-text documents using a deep learning model called REDAffectiveLM. We leverage context-specific and affect enriched representations by using a transformer-based pre-trained language model in tandem with affect enriched Bi-LSTM+Attention.
  arXiv  Detail & Related papers  (2023-01-21T19:28:25Z)
• Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa [23.00810941211685]
  Aspect-Based Sentiment Analysis (ABSA) is a fine-grained task in the field of sentiment analysis. Recent DeBERTa model (Decoding-enhanced BERT with disentangled attention) to solve Aspect-Based Sentiment Analysis problem.
  arXiv  Detail & Related papers  (2022-07-06T03:50:31Z)
• A New Generation of Perspective API: Efficient Multilingual Character-level Transformers [66.9176610388952]
  We present the fundamentals behind the next version of the Perspective API from Google Jigsaw. At the heart of the approach is a single multilingual token-free Charformer model. We demonstrate that by forgoing static vocabularies, we gain flexibility across a variety of settings.
  arXiv  Detail & Related papers  (2022-02-22T20:55:31Z)
• VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer [76.3906723777229]
  We present VidLanKD, a video-language knowledge distillation method for improving language understanding. We train a multi-modal teacher model on a video-text dataset, and then transfer its knowledge to a student language model with a text dataset. In our experiments, VidLanKD achieves consistent improvements over text-only language models and vokenization models.
  arXiv  Detail & Related papers  (2021-07-06T15:41:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.