{'fraction_non_alphanumeric': 0.011879049676025918, 'fraction_numerical': 0.0, 'mean_word_length': 5.18, '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}}
An Empirical Study of Deep Neural Networks Models for Sentiment Classification on Movie Reviews

Sentiment classification is one of the new absorbing parts appeared in natural language processing with the emergence of community sites on the web. Taking advantage of the amount of information now available, research and industry have been seeking ways to automatically analyze the sentiments expressed in texts. The challenge for this task is the human language ambiguity, and also the lack of labeled data. In order to solve this issue, Deep learning models appeared to be effective due to their automatic learning capability. In this paper, we provide a comparative study on IMDB movie review dataset, we compare word embeddings methods and further deep learning models on sentiment analysis and give broad empirical outcomes for those keen on taking advantage of deep learning for sentiment analysis in real-world settings.