Development of argument based opinion mining model with sentimental data analysis from twitter content

In present scenario, social networks have developed massive in practice and society impact. Specifically, micro-blogging is on trend in various platforms, such as Twitter, Instagram to evaluate public opinions for various issues. In recent times, some methods are developed for evaluating Twitter messages, based on the sentiment and opinions presented in tweets, corresponding to the hash-tags and keywords. However, these models have some issues in handling the contradictory content and inconsistent data. Considering with this, this article presents an argument based opinion mining model with sentimental data analysis, for extracting specific argument, which is assessed in bottom-up manner from the content from society emotion's reflects on the messages. Moreover, this model makes the user to pull out the arguments from a document set, which contains content from commercial sites, to extract the mostly argued positive and negative content. This model use natural language processing techniques, extraction of argument words for defining the decisions. The classification Naive Bayes classification is used for categorizing the results widely under agreed or disagreed. The experimental results prove that the proposed model provides feasible and appropriate results in argument analysis from Twitter content.

Automated summary

This article presents an argument mining model with sentimental data analysis to extract specific arguments from Twitter content, using natural language processing techniques for decision-making and classification.