{'fraction_non_alphanumeric': 0.015819209039548022, 'fraction_numerical': 0.0, 'mean_word_length': 5.374100719424461, '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': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Sentiment Analysis Using Machine Learning For Twitter

With the advancement of Web technology and its growth, there's a large volume of knowledge present within the Web for Internet users and plenty of knowledge is generated too. Social networking sites like Twitter, Facebook, Google+ are rapidly gaining popularity as they permit people to share and express their views about topics, have discussion with different communities, or post messages across the planet. There has been plenty of labor worn out the sphere of sentiment analysis of twitter data. we have mainly thought on sentiment analysis of twitter data which is beneficial to research the information available on the tweets where opinions are highly unstructured, heterogeneous and are may be positive or negative, or neutral accordingly. We’ve also discussed general challenges and applications of sentiment analysis on