Clustering Based Sentiment Analysis Using Randomized Clustering Cuckoo Search Algorithm

Nowadays online social networks have become one of the most important platforms, where people all over the world express their opinions, feelings, and their own experience. They do so either by texting or using images, emoji, and videos. Sentiment analysis of social media data is very important in making decisions in different areas. For example, corporates need to know what people feel regarding their products, governments need to understand public opinion towards certain decisions. In this paper, we designed an architecture that can be used to analyze social media text data sentiments based on their clustering. The suggested architecture composed from three main components namely: data cleaning, similarity finding, and randomized clustering Cuckoo search (RCCS). A formula that combine the similarity degree is suggested to improve the accuracy. As well, we utilized the power of the Cuckoo Search with the Levy flight algorithm to cluster the text data. Our architecture is used to detect the optimal or near-optimal number of clusters that best describe a text dataset. To test our model, we used the Niek Sanders tweets dataset. The proposed model achieved better performance comparing it with the other six algorithms. The six algorithms involved in our comparisons are K-Means, Latent Dirichlet Allocation (LDA), Scalable Multi-stage Clustering (SMSC), and Grouping Like-minded people using Interests Centers GLIC algorithm with its three different variations. According to our experiments, we claim that our model is efficient and very helpful in the sentiment analysis of social media text data