Predicting Collective Synchronous State of Sentiments for Users in Social Media
The increasing use of social media offers researchers with an opportunity to apply the sentiment analysis techniques over the data collected from social media websites. These techniques promise to provide an insight into the users’ perspectives on many areas. In this research, a sentiment analysis model is proposed based on HMC (Hidden Markov Chains) and K-Means algorithm to predict the collective synchronous state of sentiments for users on social media. HMC are used to find the converged state while K-Means is used to find the representative group of users. For this purpose, we have used data from a well-known social media site, Twitter, which consists of the tweets about a famous political party in Pakistan. The time series sequences of sentiments, of each user are passed on to the system to perform temporal analysis. The clustering with three and four number of clusters are found to be significant giving the representative groups. With three clusters, the representative group constitute of 82% of users and with four clusters, two representative groups are found having 45 and 36% of users. Analyzing these groups helps in finding the most popular behavior of users towards the concerned political party. Moreover, the groups perhaps tend to influence the opinion of other users in the network causing changes in their sentiments towards this party. The experimental results show that the proposed model has the power to distinguish behavior patterns of different individuals in a network.