177 research outputs found
Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text
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
BLP 2023 Task 2: Sentiment Analysis
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 domainComment: Accepted in BLP Workshop at EMNLP-2
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