{'fraction_non_alphanumeric': 0.016260162601626018, 'fraction_numerical': 0.0, 'mean_word_length': 5.350515463917525, '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}}
Extracting sentiments from reviews: A lexicon-based approach

In last decade, the field of information extraction and retrieval has increased exponentially. Sentiment analysis is a task to identify the polarity of given content. Extracting the useful content from the opinion sources becomes a challenging task. This paper used lexicon based approach for classifying a review document as positive, negative or neutral. This paper extracts the sentiments from customer reviews and SentiWordNet is used to assign the polarity to each sentiment. The classification of review document is predicted by sentimental score.