| Summary: | People nowadays use emoticons in their text increasingly in order to express their feelings or recapitulate their words. Earlier machine learning techniques only involve the classification of text, emoticons or images solely where emoticons with text have always been neglected, thus ignored lots of emotions. This research proposed an algorithm and method for sentiment analysis using both text and emoticon. In this work, both modes of data were analyzed in combined and separately with both machine learning and deep learning algorithms for finding sentiments from twitter based airline data using several features such as TF–IDF, Bag of words, N-gram, and emoticon lexicons. This research demonstrates that whenever emoticons are used, their associated sentiment dominates the sentiment conveyed by textual data analysis. Also, deep learning algorithms are found to be better than machine learning algorithms.
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