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 po