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L3Cube-MahaSent-MD: A Multi-domain Marathi Sentiment Analysis Dataset and Transformer Models

2023-06-24 · Aabha Pingle, Aditya Vyawahare, Isha Joshi, Rahul Tangsali, Raviraj Joshi

The exploration of sentiment analysis in low-resource languages, such as Marathi, has been limited due to the availability of suitable datasets. In this work, we present L3Cube-MahaSent-MD, a multi-domain Marathi sentiment analysis dataset, with four different domains - movie reviews, general tweets, TV show subtitles, and political tweets. The dataset consists of around 60,000 manually tagged samples covering 3 distinct sentiments - positive, negative, and neutral. We create a sub-dataset for each domain comprising 15k samples. The MahaSent-MD is the first comprehensive multi-domain sentiment analysis dataset within the Indic sentiment landscape. We fine-tune different monolingual and multilingual BERT models on these datasets and report the best accuracy with the MahaBERT model. We also present an extensive in-domain and cross-domain analysis thus highlighting the need for low-resource multi-domain datasets. The data and models are available at https://github.com/l3cube-pune/MarathiNLP .

📄 PDF Abstract BibTeX arXiv:2306.13888

Code (1)

l3cube-pune/MarathiNLP 공식 구현

Tasks

Sentiment Analysis

Methods 이 논문이 사용한 방법론

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Multi-Head Attention 설명 없음
Attention 설명 없음
Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…
Attention Dropout Attention Dropout is a type of dropout used in attention-based architectures, where elements are randomly dropped out of the…
WordPiece 설명 없음
Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

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