This is a preprint of a contribution published in International Conference on Applications of Natural Language to Information Systems NLDB 2016: Natural Language Processing and Information Systems The final authenticated version is available online at: https://link.springer.com/chapter/10.1007/978-3-319-41754- 7_48#citeas Cite this paper as: Teh P.L., Rayson P., Pak I., Piao S., Yeng S.M. (2016) Reversing the Polarity with Emoticons. In: Métais E., Meziane F., Saraee M., Sugumaran V., Vadera S. (eds) Natural Language Processing and Information Systems. NLDB 2016. Lecture Notes in Computer Science, vol 9612. Springer, Cham https://doi.org/10.1007/978-3-319-41754-7_48 Reversing the Polarity with Emoticons Abstract Technology advancement in social media software allows users to include elements of visual communication in textual settings. Emoticons are widely used as visual representations of emotion and body expressions. However, the assignment of values to the “emoticons” in current sentiment analysis tools is still at a very early stage. This paper presents our experiments in which we study the impact of positive and negative emoticons on the classifications by fifteen different sentiment tools. The “smiley” :) and the “sad” emoticon :( and raw-text are compared to verify the degrees of sentiment polarity levels. Questionnaires were used to collect human ratings of the positive and negative values of a set of sample comments that end with these emoticons. Our results show that emoticons used in sentences are able to reverse the polarity of their true sentiment values. The assignment of values to the “emoticons” in current sentiment analysis tools is still at a very initial stage. This paper presents our experiments in which we study the impact of positive and negative emoticons on the classification of twelve different sentiment tools. The “smiley” :) and the “sad” emoticon :( and the raw-text are compared to verify the degrees of sentiment polarity levels. Questionnaires were used to collect human ratings of the positive and negative values of a set of sample comments that end with these emoticons. Mobile and online text message platforms such as SMS (Short Message Service), Whatsapp, Wechat, LINE, Kakao Talk, Facebook Messenger, Twitter include items known as “Emoji” or “Facemarks”. Emoticons have been used as an alternative way of representing the body language or facial expression of the message author. Messages of social media users are normally written in plain text, and they are usually in an informal style and may contain grammatical errors (Yang, 2009). Unlike in face-to-face communications where sentiment can often be determined from visual cues such as body language, speech