Detection of Emotions in Hindi-English Code Mixed Text Data
This addresses the problem of emotion analysis for users of Hindi-English code-mixed text on social media, but it appears incremental as it applies existing methods to a new dataset without novel methodological contributions.
The paper tackled emotion detection in Hindi-English code-mixed text, a common form of communication on social networks, by applying state-of-the-art NLP models to classify sentences into angry, fear, happy, or sad emotions, but no concrete performance numbers were provided in the abstract.
In recent times, we have seen an increased use of text chat for communication on social networks and smartphones. This particularly involves the use of Hindi-English code-mixed text which contains words which are not recognized in English vocabulary. We have worked on detecting emotions in these mixed data and classify the sentences in human emotions which are angry, fear, happy or sad. We have used state of the art natural language processing models and compared their performance on the dataset comprising sentences in this mixed data. The dataset was collected and annotated from sources and then used to train the models.