EMOJI TEXT BASED CHATBOT MUSIC RECOMMENDATION SYSTEM USING MACHINE LEARNING

Abstract : Emojis are used in Computer Mediated Communication (CMC) as a way to express paralinguistics otherwise missing from text, such as facial expressions or gestures. However, finding an emoji on the ever expanding emoji list is a linear search problem and most users end up using a small subset of emojis that are near the top of the emoji list. Current solutions such as the search bar, or simple recommendations still requires effort from the user or does not offer a wide range of emojis. In order to understand how people use emojis, a literature review was carried out for articles that categorise emoji functions. From these, 6 functions were mentioned repeatedly: emphasis, illocutionary, social, content, aesthetic, and reaction. Illocutionary and social emojis make up the bulk of emojis that accompany text. Two main emoji recommendation models were built. One which recommends emojis similar in meaning to the text input (Related model), and another which recommends only the most common emojis (Most Used). The outputs of the two models were combined to form a third model (Combined). A between-within subjects text-based experiment was carried out over Discord. Participants’ emoji user behaviour was compared between a without recommender and a with recommender condition (within subjects). Furthermore, the three models were tested against each other in the with recommender condition (between subjects). The Related and Combined model were perceived well, while the Most Used did not always recommend appropriate emojis. Participants did use more emojis as well as a larger variety of emojis when an emoji recommender is present, however, this may be largely due to the design of the experiment. When a recommender is included on the phone emoji keyboard, the effect may be much smaller.
 EXISTING SYSTEM :
 • The paper by Hafeez Kabini Et Al suggested the problem of the existing methods to handle only deliberately displayed and exaggerated expressions of prototypical emotions despite the fact that deliberate behavior differs in visual appearance, audio profile, and timing from spontaneously occurring behavior, by taking efforts to develop algorithms that can process naturally occurring human affective behavior have recently emerged. • They also introduced and researched these recent information and discussed human emotion perception from a psychological perspective • In order to enhance this system, we have developed a pretty accurate emotion based music player which aims at counteracting a user’s negative emotions by selecting appropriate songs based on the user’s mood and several other parameters.
 DISADVANTAGE :
 • Much of the work in emoji prediction approaches it as a classification problem where sentences using only one emoji out of a short list of up to 20 emojis are used as input, using the sole emoji as the label (Barbieri, Ballesteros, & Saggion, 2017 • Liebeskind & Liebeskind, 2019; Xie, Liu, Yan, & Sun, 2016). Barbieri, Marujo, Karuturi, Brendel, and Saggion (2018) expanded the list of emoji to 300 as well as including the time of the year the emoji was used as a feature in their mode • Accuracy-related measures may be tricky to define for the present problem as everything apart from true positives are not so straightforward.
 PROPOSED SYSTEM :
 ? The accuracy of the existing system is only 70% ? Also the existing model only recommends the songs only on the basis of mood of the individual. ? As a part of the work, emoji detection is less. ? Chatbot predict only if the grammar and slang is correct ? We have used IBM Emotion Analyser to detect the mood of the user if the user uses emojis in the chat. ? We have also used Fm API which is similar to Spotify API ? By adding more attributes like Emoji detection, Emotions, Music recommendations, the Chatbot will give more accuracy compared to the existing system. ? We have used the Natural Language Processor and Machine Learning approach to increase the accuracy of the system. ? Emoji plays a vital role to detect emotion of the person ? We are going to classify the emoji and text to detect the mood of the user ? We are providing counselling if the user is found depressed.
 ADVANTAGE :
 • Emojis are used in Computer Mediated Communication (CMC) as a way to express paralinguistics otherwise missing from text, such as facial expressions or gestures. However, finding an emoji on the ever expanding emoji list is a linear search problem and most users end up using a small subset of emojis that are near the top of the emoji list. • Most Used did not always recommend appropriate emojis. • During text-based communication, punctuation is traditionally used to mark much of how the text should be read. For instance, commas add pauses, exclamation marks convey emphasis and perhaps an increase in volume • More recently, emojis have become a staple in instant messaging used to add flair as well as emotions to text. • A survey was conducted to get an understanding of emoji variability between users as well as to collect an independent test set of text messages with emojis that can be used for offline evaluation of the models. In the end, three recommenders were made.
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