CDNB: CAVIAR-Dragonfly Optimization with Naive Bayes for the Sentiment and Affect Analysis in Social Media

With the advent of the new information technologies, the growth of online reviews regarding an organization or a company or any other sector has been playing a vital role in improving the sector plans and decisions. The vast significance of the online reviews that determine the sentiment polarity is the hectic challenge of the current scenario. Sentiment classification is a process of classifying the text according to the sentimental polarities of opinions, which has positive or negative. Thus, this article concentrates on presenting a novel method, named CAVIAR-Dragonfly optimization with Extended Naive Bayes (CDNB), for performing sentiment classification and affective state classification. At first, the BITS review from Twitter is subjected to preprocessing, which includes stop word removal and stemming. Then, the next step is the feature extraction, in which all the reviews are converted to a feature vector. After that, all the individual feature vectors are collected to form the feature matrix, which is applied to the proposed C-Dragonfly optimization algorithm, to perform the sentiment classification and affective state classification. The performance of the proposed method is analyzed using the Twitter Sentiment Analysis Training Corpus Data Set based on true positive rate (TPR), true negative rate (TNR), and accuracy. From the analysis, it can be shown that the proposed method yields the maximum TPR, TNR, and accuracy of 89.0934%, 72.3064%, and 79.3591% for sentiment classification and 84.2122%, 66.2187%, and 76.6249% for the sentiment affective state classification.