I ndic F ed: A Federated Approach for Sentiment Analysis in Indic Languages (2021)

Abstract: The task of sentiment analysis has been extensively studied in high-resource languages. Even though sentiment analysis is studied for some resource-constrained languages, the corpora and the datasets available in other low resource languages are scarce and fragmented. This prevents further research of resource-constrained languages and also inhibits model performance for these languages. Privacy concerns may also be raised while aggregating some datasets for training central models. Our work tries to steer the research of sentiment analysis for resource-constrained languages in the direction of Federated Learning. We conduct various experiments to compare server based and federated approaches for 4 Indic Languages - Marathi, Hindi, Bengali, and Telugu. Specifically, we show that a privacy preserving approach, Federated Learning surpasses traditional server trained LSTM model and exhibits comparable performance to other servers-side transformer models.

## introduction with the proliferation of opinionated user data on social media platforms @xcite , capturing user emotions could help in decision making and determining public opinion on cultural, social, and political agendas @xcite . this has prompted research into sentiment analysis and opinion mining for english (e.g. @xcite @xcite @xcite @xcite @xcite , which is aided by the availability of large-scale, centralized training datasets. however, there is a pressing need to work on nlp beyond resource-rich languages due to cultural, linguistic, and societal factors @xcite . ## datasets we use publicly available datasets for sentiment analysis in low-resource indian languages. in case of actsa dataset (mukku and mamidi, 2017) and hindi sentiment analysis dataset 1 , we make a stratified split of 70:15:15 to divide the data into train, test, and development sets. for other datasets, we use the original splits which are provided. ## experiments all the experiments are conducted on google colab using a nvidia tesla p100 gpu (16 gb) with 26 gb ram. the metrics used to compare the results are weighted auc, weighted f1 and the accuracy score for every model in every variation. ## https://spacy.io/ to make the computation cheaper on resourceconstrained edge-devices, by the distributed training process, we train it on a basic bi-lstm model @xcite with 2 hidden layers and dropout @xcite set to 0.5. different client fractions are used to observe the variation of results in the federated setting too. the client fractions of 10%, 30% and 50% are considered and these clients are always picked randomly for every round. all the models are run for 500 rounds with early stopping applied on the average training loss. the learning rate is set to 0.01 and the proximal term is set to 0.01 as default @xcite . ## results telugu (te): from ## conclusion we show that a lstm model trained using federated learning can outperform an identical server trained lstm model for 4 indic languages -marathi, bengali, telugu and hindi. we also show that federated learning achieves comparable performance to other server trained transformer ar-chitectures 4 . surprisingly, we find that for smaller datasets, lower client fractions show better performance. to our knowledge, this represents one of the first applications of federated learning in lowresource settings for sentiment analysis. federated learning offers security and privacy advantages for users by training across a population of highly distributed computing devices while simultaneously improving model performance. for future work, it would be interesting to train heavier transformer models like indicbert, xlm-r, etc. using federated learning which could help to minimize the large gap in accuracy in non-iid settings. conducting some interpretable evaluation on the intermediate models before updating during federated training is another important future direction.