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| Exploring Text-Based Emotions Recognition Machine Learning Techniques On Social Media Conversation. |
Sherzod Yarashev, Elyor G'aybulloyev, Begzod Erkinov, Temur Ochilov, Jurabek Abdiyev
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Abstract:
Emotions hold a paramount role in the conversation, as it expresses context to the conversation. Text/word in conversation consists of lexical and contextual meanings. Extracting emotions from text has been an interesting work recent thees years. With the advancement of machine learning techniques and hardware to support the machine learning process, recognising emotions from a text with machine learning provides promising and significant results. This research aims to explore several popular machine learning to recognise emotions from a conversation in social media. The algorithms proposed in this research are ranged from traditional machine learning to deep learning techniques. The dataset used in this paper is provided by A?ectiveTweets, with a baseline of F1S core of 0.71 with word N-grams and SentiStrength. The research contributes extensive explorations in a number of machine learning algorithms, resulting in a total of 2302 features sets were explored, where each features sets has 100-1000 features extracted from the text. The results demonstrate Generalised Linear Model provides the best Accuracy score (0.92), Recall (0.902), Precision (0.902), F1 score (0.901) with standard deviation of accuracy of ±1, 2%.
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