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| Exploring Text-based Emotions Recognition Machine Learning Techniques on Social Media Conversation |
Sherzod Yarashev, Bekzod Erkinov, Elyor G'aybulloyev, Jurabek Abdiyev, Nigora Abdiyeva, Ruslan Malikov
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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 these years. With the advancement of machine learning techniques and hardware to support the machine learning process, recognizing emotions from a text with machine learning provides promising and significant results. This research aims to explore several popular machine learning to recognize 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?ective Tweets, with a baseline of F1S core of 0.71 with word N-grams and Senti Strength. 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 Generalized 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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