Summarization on Text Mining

Abstract

In this scenario, we focus on multimodal news aggregation retrieval and fusion. In particular, we present preliminary experiments aimed at automatically suggesting keywords to news and news aggregations. The proposed solution is based on the adoption of extraction-based text summarization techniques. Experiments are aimed at comparing the selected text summarization techniques with respect to a simple technique based on part-ofspeech tagging. Results show that the proposed solution performs better than the baseline solution in terms of precision, recall, and F1. For example, human sentiments can be positive, negative. Now a Days we highly consider opinions of friends, domain experts for decision making in day today's life. Natural language techniques are applied to extract emotions from unstructured data. In marketing and advertising domains Opinion Mining being larger domain. The advertiser required to the analyze performance/ ads status that person posted on site. Star rating based on mechanism may go fraud, automatic robots or responders. So, the
present system required to analyze applying NLP & comments. Fraud comments could indifferent through applying irrelevant comment elimination mechanism suggested in the paper. In that paper the role and importance of opinions on public are discussed especially. Various techniques that proposed and emerged to discuss about the opinions are mentioned in details.