Review of Feature Selection and Optimization Strategies in Opinion Mining (Global Journal of Computer Science and Technology, 2016)
Opinion mining and sentiment analysis methods has become a prerogative models in terms of gaining insights from the huge volume of data that is being generated from vivid sources. There are vivid range of data that is being generated from varied sources. If such veracity and variety of data can be explored in terms of evaluating the opinion mining process, it could help the target groups in getting the public pulse which could support them in taking informed decisions. Though the process of opinion mining and sentiment analysis has been one of the hot topics focused upon by the researchers, the process has not been completely revolutionary. In this study the focus has been upon reviewing varied range of models and solutions that are proposed for sentiment analysis and opinion mining. From the vivid range of inputs that are gathered and the detailed study that is carried out, it is evident that the current models are still in complex terms of evaluation and result fetching, due to constraints like comprehensive knowledge and natural language limitation factors. As a futuristic model in the domain, the process of adapting scope of evolutionary computational methods and adapting hybridization of such methods for feature extraction as an idea is tossed in this paper.
Keywords: opinion mining; sentiment analysis; social web data; machine learning; social media