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 12th among 30 teams that participated in the competition.

本文讨论了 nlpBDpatriots 参加首届与 EMNLP 合作举办的 Bangla 语言处理（BLP）研讨会上共享任务的社交媒体帖子情感分析。我们使用孟加拉语数据集，该数据集由共享任务组织者提供，并注有积极、中性和消极标签，旨在确定社交媒体内容的极性。我们最好的系统是数据增强的迁移学习方法，其 micro F1 分数达到 0.71。在参加比赛的 30 支球队中，我们的最好系统排名第 12 位。

nlpBDpatriots 在 BLP-2023 任务 2 中的应用：孟加拉情感分析的迁移学习方法

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

In this paper, we discuss the nlpBDpatriots entry to the shared task on
Violence Inciting Text Detection (VITD) organized as part of the first workshop
on Bangla Language Processing (BLP) co-located with EMNLP. The aim of this task
is to identify and classify the violent threats, that provoke further unlawful
violent acts. Our best-performing approach for the task is two-step
classification using back translation and multilinguality which ranked 6th out
of 27 teams with a macro F1 score of 0.74.

我们讨论了 nlpBDpatriots 参与的暴力煽动文本检测共享任务，并通过两步分类使用后向翻译和多语种方法，在 27 个团队中排名第 6，宏观 F1 得分为 0.74。