Emotion Detection from text is a recent field of research that is closely related to Sentiment Analysis. Emotion Analysis aims to detect and recognize different types of feelings through the expression of texts, such as anger, disgust, fear, happiness, sadness, surprise etc. Identifying emotion information from social media, news articles and other user generated content has a lot of applications. Current techniques heavily depend on emotion and polarity lexicons; however, such lexicons are only available in few resource rich languages and this hinders the research for resource scarce languages. Also, social media texts in Indian languages have distinct features such as Romanization, code mixing, grammatical and spelling mistakes, which makes the task of classification even harder. This research addresses this task by training a deep learning architecture on large amount of data available on social media platforms like Twitter, using emojis as proxy for emotions. The model’s performance is then evaluated on a manually annotated dataset. This work is focused on Hindi language but the techniques used are language agnostic and can be used for other languages as well.
@InProceedings{JAIN18.1, author = {Royal Jain}, title = {Anger Detection in Social Media for Resource Scarce Languages}, booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {may}, date = {7-12}, location = {Miyazaki, Japan}, editor = {Girish Nath Jha and Kalika Bali and Sobha L and Atul
Kr. Ojha}, publisher = {European Language Resources Association (ELRA)}, address = {Paris, France}, isbn = {979-10-95546-09-2}, language = {english} }
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