Opinion Mining on Social Media Text Using Optimized Deep Belief Networks

In the digital world, most people spend their leisure and precious time on social media networks such as Facebook, Twitter. Instagram, and so on. Moreover, users post their views of products, services, political parties on their social sites. This information is viewed by many other users and brands. With the aid of these posts and tweets, the emotions, polarities of users are extracted to obtain the opinion about products or services. To analyze these posts sentiment analysis or opinion mining techniques are applied. Subsequently, this field rapidly attracts many researchers to conduct their research work due to the availability of an enormous number of data on social media networks. Further, this method can also be used to analyze the text to extract the sentiments which are classified as moderate, neutral, low extreme, and high extreme. However, the extraction of sentiment is an arduous one from the social media datasets, since it includes formal and informal texts, emojis, symbols. Hence to extract the feature vector from the accessed social media datasets and to perform accurate classification to group the texts based on the appropriate sentiments we proposed a novel method known as, Deep Belief Network-based Dynamic Grouping-based Cooperative optimization method DBN based DGCO. Exploiting this method the data are preprocessed to attain the required format of text and henceforth the feature vectors are extracted by the ICS algorithm. Furthermore, the extracted datasets are classified and grouped into moderate, neutral, low extreme, and high extreme with DBN based DGCO method. For experimental analysis, we have taken two social media datasets and analyzed the performance of the proposed method in terms of performance metrics such as accuracy/precision, recall, F1 Score, and ROC with HEMOS, WOA-SITO, PDCNN, and NB-LSVC state-of-art works. The acquired accuracy/precision, recall, and F1 Score, of our proposed ICS-DBN-DGCO method, are 89%, 80%, 98.2%, respectively.