Document from the year 2019 in the subject Communications - Public Relations, Advertising, Marketing, Social Media, , language: E...
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Document from the year 2019 in the subject Communications - Public Relations, Advertising, Marketing, Social Media, , language: English, abstract: Social media content has stirred much excitement and created abundant opportunities for understanding the opinions of the general public and consumers toward social events, political movements, company strategies, marketing campaigns, and product preferences. Many new and exciting social, geo political, and business-related research questions can be answered by analyzing the thousands, even millions, of comments and responses expressed in various blogs, forums, social media and social network sites, virtual worlds, and tweets. This is one of the good medium to explore the opinion of people about the particular event and so that this may help in the making any business decisions or the feedback about political activities to be carried out in future. Therefore, we extracted the real time tweets on the social tweet keyword from the twitter web site, news website etc. using the twitter 4j Libraries and their API's and JSOUP Libraries for obtaining the real time tweets from the respective web sites for English keyword only. These tweets are preprocessed and obtained the keyword related sentences only. These preprocessed tweets further used for the removal of slang, hash, tags and URL and the removal of stop words. We also used the abbreviations and emoticon conversion to get corresponding complete meaning full message from tweets. The processed tweets are further classified using three unstructured models EEC, IPC and SWNC. The results of these models are compared by obtaining the confusion matrix and their parameter such as precision, recall and accuracy. The SWNC model showed good result of classification over the EEC and IPC. Further the Hybrid model is used to reduce the number of the neutral tweets and obtained the corresponding results and shown by pie graph. By comparing the results of the SWNC model and the Hybrid model, it is observed that the numbers of neutral tweets are reduced in Hybrid model. The % range of reduction is around 20 - 25% in comparison with the SWNC model. Thus, we classified the real time social tweets using unstructured and their hybrid models. For obtaining these results, we developed the windows based indigenous, integrated and user friendly application in java and using NetBean's framework.