Impact of Feature Selection for Emotion Detection from Annotated Punjabi Text
Keywords:
Natural Language Processing, Feature Extraction, Feature Selection, Grasshopper Optimization Algorithm, Emotion DetectionAbstract
Natural language processing means dealing with data that is understood by humans but not directly understood by machines. Firstly, there is a need to convert that data into a form of numeric data because machines are able to understand and process numeric data. This procedure of data conversion is called feature extraction from text data. In this paper, various feature extraction techniques along with text similarity methods are used to extract numeric values from already processed and cleansed data. These techniques are term frequency-inverse document frequency, along with cosine similarity, jaccard similarity, and euclidean distance methods of text similarity. So, in this way, features are extracted from annotated emotional Punjabi text data for system training and testing for the classification process. In this article, a novel system is proposed for feature selection after feature extraction. The most relevant feature sets are selected by the grasshopper optimization algorithm and provided to the classification model for system training and testing. Comparison of results after feature extraction and after feature selection is done under various statistical performance measures, and these are satisfactory.
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