Finding Related Posts On Social Media Through Content Semantic Similarity
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Abstract
Sentiment research on social media provides businesses with a quick and easy way to track public opinion about their brand, business, directors, and other topics. In recent years, a variety of features and approaches for training sentiment classifiers on datasets have been investigated, with mixed results. In this research, we have proposed an approach for detecting emotion in text and predicting sentiment using semantics as extra characteristics for various datasets and a study on present methods for opinion mining Forum posts has the specific problem of finding related posts to a post at hand. By considering across the related documents the contents of posts are generally consider are whole .Here similarity process are done between two posts with respective segments and should be of same intention. All posts are generally fragmented in the form of group to attain the goal bunches. Now similarities are generally cross view in the forums in the form of sections and that will of same intention. Finding related forum posts are done in the form of division strategy is delineated
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References
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