地球资源数据云——数据资源详情
泰米尔语SentiMix NLP
该数据集《TamilSentiMix NLP》主要用于多分类任务，数据形态以图像为主，应用场景偏向文本内容分析。 题目说明：Tamil - English code - switched sentiment - annotated corpus containing 15,744 comment 任务类型：图像多分类。 建议流程：先检查类别分布与脏样本，再用迁移学习（如 ResNet/EfficientNet）建立基线。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：mcs_ds_edited_iter_shuffled.csv。 Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision - making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non - native scripts. Non - availability of annotated code - mixed data for a low - resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil - English code - switched, sentiment - annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter - annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark. Introductory Paper Corpus Creation for Sentiment Analysis in Code - Mixed Tamil - English Text
摘要概览
该数据集《TamilSentiMix NLP》主要用于多分类任务，数据形态以图像为主，应用场景偏向文本内容分析。 题目说明：Tamil - English code - switched sentiment - annotated corpus containing 15,744 comment
任务类型：图像多分类。
建议流程：先检查类别分布与脏样本，再用迁移学习（如 ResNet/EfficientNet）建立基线。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：mcs_ds_edited_iter_shuffled.csv。
Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision - making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments.
However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non - native scripts. Non - availability of annotated code - mixed data for a low - resourced language like Tamil also adds difficulty to this problem.
To overcome this, we created a gold standard Tamil - English code - switched, sentiment - annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter - annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.
Introductory Paper
Corpus Creation for Sentiment Analysis in Code - Mixed Tamil - English Text