Noise Destruction Towards Quality Improvement in Emotion Recognition from Text Using Pre-Processing Modules

Abstract

In evaluating internet circles, the dataset in textual form turns out to be an important tool for communication. Recognizing user’s emotion-based textual data is a difficult task. The original big textual data contains lots of noisy information, which may lead to degrading the system performance. So, in this research, novel hybrid based pre-processing approaches are employed to overcome these issues. Initially, a big text dataset is fed into the pre-processing stage, which carries a lot of hybrid approaches for eliminating noisy contents. Some steps are followed to carry out pre-processing steps, which are lower case conversion, HTML/XML tag removal, stop word removal, Tokenization, Text normalization, and numerical conversion. Afterwards, the string information is transformed into numerical values for extracting the feature information. The pre-processing steps make the final classifier step easier and achieve a good prediction measure with the minimum error value. The proposed methodology is validated with three conventional classifier algorithms, namely PNN, ANN and k‑NN. At last, the performance metrics are evaluated to show the effectiveness of the proposed methodology.