{'fraction_non_alphanumeric': 0.028368794326241134, 'fraction_numerical': 0.008865248226950355, 'mean_word_length': 5.2032967032967035, '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}}
SAEKCS: Sentiment Analysis for English – Kannada Code SwitchText Using Deep Learning Techniques

Usage of social media has become more widespread to express sentiment, emotion about public events, government policies, product reviews etc. Performing Sentiment Analysis (SA) on social media data will give more and more insights about user’s behavior. Multilingual society like India, it is very common to use code switch text in social media to express their views. Switching between languages while communicating is refer as code mixing or code switching. Analyzing this code switch text and getting the useful information from this too harder because of its unstructured linguistic nature. In this paper, we proposed a hybrid model called SAEKCS for sentiment analysis on Kannada-English code switch text. Our proposed model uses deep learning techniques like Convolutional Neural Network (CNN) and Bidirectional Long Short Term Memory (BiLSTM) for sentiment analysis in code switch text. Our experimental results shows that 77.6% of accuracy and 69.6% of coverage. These results are much better than existing works [17] [18].