{'fraction_non_alphanumeric': 0.022169437846397466, 'fraction_numerical': 0.00395882818685669, 'mean_word_length': 5.257425742574258, '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}}
Deep Learning Enhanced with Graph Knowledge for Sentiment Analysis

The traditional way to address the problem of sentiment classification is based in Machine Learning techniques; however, these models are not able to grasp all the richness of the text that comes from different social media, personal web pages, blogs, etc., ignoring the semantic of the text. Knowledge Graphs give a way to extract structured knowledge from images and texts, in order to facilitate their semantic analysis. In this work, we propose a new hybrid approach for Sentiment Analysis based on Knowledge Graphs and Deep Learning techniques, to identify the sentiment polarity (positive or negative) in short documents, particularly in tweets. We represent the tweets using graphs, then graph similarity metrics and a Deep Learning classification algorithm are applied to produce sentiment predictions. This approach facilitates the traceability and explainability of the classification results, since it is possible to visually inspect the graphs. We compare our proposal with character n-gram embeddings based Deep Learning models to perform Sentiment Analysis. Results show that our proposal is able to outperforms classical n-gram models, with a recall up to 89% and F1-score of 88%.