Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{143459,
        author = {R.P.S.Manikandan and Dr. A.M. Kalpana},
        title = {Tracking and Analyzing the Public Opinions by Interpretation Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {2},
        number = {11},
        pages = {316-318},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=143459},
        abstract = {Tracking and analyzing the public interests, several numbers of users may share their opinions in the websites. These tracking and analysis can provide difficulties for to take the decisions. In the existing research, it was mainly focused on modeling the public sentiment. In our work, move one step further to interpret the sentiment variations in Knowledge Mining. In order to get the Potential Interpretations of the sentiment variations, we introduce Foreground and Background Latent Dirichlet Allocation based model to extract the essential foreground topics and filter out the existing background topics. By introducing the Reason Candidate and Background LDA (RCB-LDA) to rank them with respect to their “popularity” and within the variation period, to further enhance the readability of the mining. That is, we provide ranking to the candidate suggestions based on overall reviews. The above method is to be introduced while developing the online shopping cart to our customers in a real time mode.},
        keywords = {Latent Dirichlet Allocation, Gibbs sampling, sentiment variations.},
        month = {},
        }