{'fraction_non_alphanumeric': 0.009734513274336283, 'fraction_numerical': 0.0, 'mean_word_length': 4.9214659685863875, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Hybrid Approach for Opinion Mining Using Twitter Data

Abrupt and fast change of technology give birth to research areas which are directly related to users. NLP is such type of emerging research area in which opinions of users using technology play the important role. As with advancement in social sites people post every information. These
 posts are related to perspective of user about some company service, regarding any political party and review related to entertainment industry. In NLP, opinion mining is considered as important field because it directly related to society opinion. It can be described as that field in which
 conclusion of posted opinions are extracted. Since posted messages are short in nature and it results sparse feature matrix giving less values of performance matrix. In this research paper we have proposed Optimised Feature Opinion Classifier for Big Data termed as OFOCBD which works on the
 principle of optimizing the classifiers giving improved values of performance matrix. In this research paper our proposed algorithms have shown better results as the data is converted into big data.