RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and Majority Voted Fine-Tuned Transformers

• URL: http://arxiv.org/abs/2310.14261v1
• Date: Sun, 22 Oct 2023 10:55:56 GMT
• Title: RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and Majority Voted Fine-Tuned Transformers
• Authors: Pratinav Seth, Rashi Goel, Komal Mathur and Swetha Vemulapalli
• Abstract summary: This paper describes our approach to submissions made at BLP Workshop - Sentiment Analysis of Bangla Social Media Posts. Our system scored 0.711 for the multiclass classification task and scored 10th place among the participants on the leaderboard for the shared task.
• Score: 2.048226951354646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper describes our approach to submissions made at Shared Task 2 at BLP Workshop - Sentiment Analysis of Bangla Social Media Posts. Sentiment Analysis is an action research area in the digital age. With the rapid and constant growth of online social media sites and services and the increasing amount of textual data, the application of automatic Sentiment Analysis is on the rise. However, most of the research in this domain is based on the English language. Despite being the world's sixth most widely spoken language, little work has been done in Bangla. This task aims to promote work on Bangla Sentiment Analysis while identifying the polarity of social media content by determining whether the sentiment expressed in the text is Positive, Negative, or Neutral. Our approach consists of experimenting and finetuning various multilingual and pre-trained BERT-based models on our downstream tasks and using a Majority Voting and Weighted ensemble model that outperforms individual baseline model scores. Our system scored 0.711 for the multiclass classification task and scored 10th place among the participants on the leaderboard for the shared task. Our code is available at https://github.com/ptnv-s/RSM-NLP-BLP-Task2 .

Related papers

• Narrative Action Evaluation with Prompt-Guided Multimodal Interaction [60.281405999483]
  Narrative action evaluation (NAE) aims to generate professional commentary that evaluates the execution of an action. NAE is a more challenging task because it requires both narrative flexibility and evaluation rigor. We propose a prompt-guided multimodal interaction framework to facilitate the interaction between different modalities of information.
  arXiv  Detail & Related papers  (2024-04-22T17:55:07Z)
• nlpBDpatriots at BLP-2023 Task 2: A Transfer Learning Approach to Bangla Sentiment Analysis [7.3481279783709805]
  In this paper, we discuss the nlpBDpatriots entry to the shared task on Sentiment Analysis of Bangla Social Media Posts. 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. Our best system ranked 12th among 30 teams that participated in the competition.
  arXiv  Detail & Related papers  (2023-11-25T13:58:58Z)
• LowResource at BLP-2023 Task 2: Leveraging BanglaBert for Low Resource Sentiment Analysis of Bangla Language [0.5922488908114022]
  This paper describes the system of the LowResource Team for Task 2 of BLP-2023. It involves conducting sentiment analysis on a dataset composed of public posts and comments from diverse social media platforms. Our primary aim is to utilize BanglaBert, a BERT model pre-trained on a large Bangla corpus.
  arXiv  Detail & Related papers  (2023-11-21T17:21:15Z)
• Understanding writing style in social media with a supervised contrastively pre-trained transformer [57.48690310135374]
  Online Social Networks serve as fertile ground for harmful behavior, ranging from hate speech to the dissemination of disinformation. We introduce the Style Transformer for Authorship Representations (STAR), trained on a large corpus derived from public sources of 4.5 x 106 authored texts. Using a support base of 8 documents of 512 tokens, we can discern authors from sets of up to 1616 authors with at least 80% accuracy.
  arXiv  Detail & Related papers  (2023-10-17T09:01:17Z)
• BanglaNLP at BLP-2023 Task 1: Benchmarking different Transformer Models for Violence Inciting Text Detection in Bengali [0.46040036610482665]
  This paper presents the system that we have developed while solving this shared task on violence inciting text detection in Bangla. We explain both the traditional and the recent approaches that we used to make our models learn. Our proposed system helps to classify if the given text contains any threat.
  arXiv  Detail & Related papers  (2023-10-16T19:35:04Z)
• BanglaNLP at BLP-2023 Task 2: Benchmarking different Transformer Models for Sentiment Analysis of Bangla Social Media Posts [0.46040036610482665]
  This paper presents our submission to Task 2 (Sentiment Analysis of Bangla Social Media Posts) of the BLP Workshop. Our quantitative results show that transfer learning really helps in better learning of the models in this low-resource language scenario. We obtain a micro-F1 of 67.02% on the test set and our performance in this shared task is ranked at 21 in the leaderboard.
  arXiv  Detail & Related papers  (2023-10-13T16:46:38Z)
• Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts [91.3755431537592]
  The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding. This study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery.
  arXiv  Detail & Related papers  (2023-07-05T20:16:20Z)
• Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis [64.70116276295609]
  SentiWSP is a Sentiment-aware pre-trained language model with combined Word-level and Sentence-level Pre-training tasks. SentiWSP achieves new state-of-the-art performance on various sentence-level and aspect-level sentiment classification benchmarks.
  arXiv  Detail & Related papers  (2022-10-18T12:25:29Z)
• A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models [2.5768647103950357]
  We provide a review of Bangla NLP tasks, resources, and tools available to the research community. We benchmark datasets collected from various platforms for nine NLP tasks using current state-of-the-art algorithms. We report our results using both individual and consolidated datasets and provide data for future research.
  arXiv  Detail & Related papers  (2021-07-08T13:49:46Z)
• LTIatCMU at SemEval-2020 Task 11: Incorporating Multi-Level Features for Multi-Granular Propaganda Span Identification [70.1903083747775]
  This paper describes our submission for the task of Propaganda Span Identification in news articles. We introduce a BERT-BiLSTM based span-level propaganda classification model that identifies which token spans within the sentence are indicative of propaganda.
  arXiv  Detail & Related papers  (2020-08-11T16:14:47Z)
• Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-Task Learning for Offensive Language Detection [55.445023584632175]
  We build an offensive language detection system, which combines multi-task learning with BERT-based models. Our model achieves 91.51% F1 score in English Sub-task A, which is comparable to the first place.
  arXiv  Detail & Related papers  (2020-04-28T11:27:24Z)

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.