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@article{143325,
        author = {B.Kishore},
        title = {Tweet Analysis for Earthquake Reporting System Development},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {2},
        number = {10},
        pages = {97-100},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=143325},
        abstract = {Twitter has gotten much consideration as of late. An essential normal for Twitter is its ongoing nature. We explore the constant collaboration of occasions, for example, tremors in Twitter and propose a calculation to screen tweets and to recognize an objective occasion. To identify an objective occasion, we devise a classifier of tweets in view of components, for example, the watchwords in a tweet, the quantity of words, and their connection. In this way, we produce a probabilistic spatiotemporal model for the objective occasion that can locate the focal point of the occasion area. We see every Twitter client as a sensor and apply molecule separating, which are generally utilized for area estimation. The molecule channel works superior to anything other similar techniques for assessing the areas of target occasions. As an application, we build up a tremor reporting framework for use in Japan. Due to the various seismic tremors and the expansive number of Twitter clients all through the nation, we can identify a quake with high likelihood (93 percent of quakes of Japan Meteorological Agency (JMA) seismic force scale 3 or more are recognized) simply by observing tweets. Our framework recognizes tremors quickly and warning is conveyed much quicker than JMA show declarations},
        keywords = {Twitter, occasion location, social sensor, area estimation, quake},
        month = {},
        }