MIM (Mobile Instant Messaging) Classification using Term Frequency-Inverse Document Frequency (TF-IDF) and Bayesian Algorithm
Journal Title: International Journal of Modern Research In Engineering & Management. - Year 2019, Vol 2, Issue 2
Abstract
The focus of the study is based on binary sentiment classification on aspect level to develop a hybrid sentiment classification framework of WhatsApp MIMs (Mobile Instant Messages). It has been carried out into two phases i.e. training phase and testing phase. The training phase, 75% data is used for training dataset. Pre-processing techniques like tokenization, removing stop words, case normalization, removing punctuation and stemming are applied to acquire cleaner dataset to be used as input. The output is sent to the classifier after applying TF-IDF for feature weighting. In the second phase, the classifier is trial with 25% testing dataset. Bernoulli’s Naïve Bayesian classifier which is an improved form of traditional Naïve Bayesian classifier is used to classify sentiments. There are 417 messages in total where 244 and 173 are classified as positive and negative respectively. The proposed model has achieved satisfactory results up to 81.73% in comparison to base-line classification model by getting 12 points higher accuracy i.e. 69.23%.
Authors and Affiliations
Kashaf-u-Duja .
Keywords
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