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

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.