Music Recommender System Using ChatBot

In this era of technological advances, text-based music recommendations are much needed as they will help humans relieve stress with soothing music according to their moods. In this project, we have implemented a chatbot that recommends music based on the user's text tone. By analyzing the tone of the text expressed by the user, we can identify the mood. Once the mood is identified, the application will play songs in the form of a web page based on the user's choice as well as his current mood. In our proposed system, themain goal is to reliably determine a user's mood based on their text tone with an application that can be installed on the user's desktop. In today's world, human computer interaction (HCI) plays a crucial role, and the most popular concept in HCI is recognition of emotion from text. As part of this process, the frontal view of the user's text is used to determine the mood. The extraction of text tone from the user's text is anotherimportant aspect. We have used IBM Analyser to check the text tone of the user and to predict the mood based on the text of the user, and Last.FM API to recommend songs based on themood of the user. Keywords: Introduction, Product-Architecture, Tone Analyzer, Music Classification Based on Mood, Acoustic Analysis, Experiment, Future/Current Use, Importance, Background, Literature Survey, Methodology, Equations, Planning, Tools and Technology, Conclusion.

Web Application for Emotion-based Music Player

Human expressions play an important role in the extraction of an individual's emotional state. It helps in determining the current state and mood of an individual, extracting and understanding the emotion that an individual has based on various features of the face such as eyes, cheeks, forehead, or even through the curve of the smile. A survey confirmed that people use Music as a form of expression. They often relate to a particular piece of music according to their emotions. Considering these aspects of how music impacts a part of the human brain and body, our project will deal with extracting the user’s facial expressions and features to determine the current mood of the user. Once the emotion is detected, a playlist of songs suitable to the mood of the user will be presented to the user. This can be a big help to alleviate the mood or simply calm the individual and can also get quicker song according to the mood, saving time from looking up different songs and parallel developing a software that can be used anywhere with the help of providing the functionality of playing music according to the emotion detected. Keywords: Music, Emotion recognition, Categorization, Recommendations, Computer vision, Camera

Automatic Personalized Interventions for Behaviour Change Towards Healthier Lifestyle

This paper outlines the results evidenced by WellCo (GA nº: 769765), an European project funded by the European Commission within its H2020 programme under the personalised medicine call. The aim of this project was to develop and validate how ICT technologies may engage people to adopt healthier behaviour choices that improve their wellbeing status for as long as possible. Using data from wearable devices and AI-based algorithms, WellCo assesses the status of the user in terms of wellbeing and the risk of CVD. Using this information, WellCo develops an affective-aware coach that empowers users in the process of change of behaviour through the provision of interventions tailored to their current mood and life context. These motivational activities ranged from recommendations, goals to achieve, interactions with people in the social network, tips from experts and supporting groups suggested by the platform and adapted to their needs. The project has been validated with ageing people in Italy, Denmark and Spain. Despite the COVID-19 situation, results are very promising in terms of the possibilities that ICT technologies have for health promotion and set the basis for further research in this direction.

Individual Differences in Frustration and Performance with Online Shopping Activities

Individual differences in user responses and interactions with technology are important to consider when examining frustration and expectations for technology performance. This research expanded on Ferreri and Mayhorn (2021) and Hadlington and Scase (2018) by examining individual differences in responses to failures in digital technology (RFDT) when exposed to a malfunction (present vs. absent) and given an expectation (no vs. low vs. high) about the technology capabilities. A preliminary sample of 30 undergraduate students was obtained to complete an online shopping task. Following the task, participants reported the items they were asked to purchase, as well as their responses to failures in digital technology, technology acceptance attitudes, personality dimensions, and current mood (pre vs. post). Several correlations revealed consistent findings with previous research and indicate potentially significant findings with the full dataset. It is anticipated that those with low expectation and scoring high in neuroticism will report the most frustration.

Emotional Marketing on Consumer Behaviour-Perception Study

<p><b>Purpose: </b>This paper is about how emotional marketing affects consumer behavior and how emotions will affect the decision-making process of the consumers. </p> <p><b>Methodology: </b>To know the mindset of the consumers about their response to different kinds of emotions, 150 samples were collected randomly using a questionnaire. The questions included how emotions affect them in various situations, which type of emotions they often feel, and how they felt after purchasing. </p> <p><b>Findings: </b>According to the estimated results it was found that emotions will play a major role in consumer behavior and it also depends on their current mood and past experiences. </p> <p><b>Limitations:</b> Though it was proved that emotions will affect consumer behavior, it was completely subjective in nature as every individual has variety of emotions based on their experiences in life and it is practically not possible to satisfy every customer's emotional need. </p> <p><b>Future research:</b> Future research can focus on various emotions and the behavioral aspects that can have an impact and a cross-sectional study can be conducted. Rather than depending on the consumer mindset at the time of buying, it would be the best option to influence consumer’s emotions using advertisements and brand image. </p>

Emotional Marketing on Consumer Behaviour-Perception Study

<p><b>Purpose: </b>This paper is about how emotional marketing affects consumer behavior and how emotions will affect the decision-making process of the consumers. </p> <p><b>Methodology: </b>To know the mindset of the consumers about their response to different kinds of emotions, 150 samples were collected randomly using a questionnaire. The questions included how emotions affect them in various situations, which type of emotions they often feel, and how they felt after purchasing. </p> <p><b>Findings: </b>According to the estimated results it was found that emotions will play a major role in consumer behavior and it also depends on their current mood and past experiences. </p> <p><b>Limitations:</b> Though it was proved that emotions will affect consumer behavior, it was completely subjective in nature as every individual has variety of emotions based on their experiences in life and it is practically not possible to satisfy every customer's emotional need. </p> <p><b>Future research:</b> Future research can focus on various emotions and the behavioral aspects that can have an impact and a cross-sectional study can be conducted. Rather than depending on the consumer mindset at the time of buying, it would be the best option to influence consumer’s emotions using advertisements and brand image. </p>

Statistical Analysis of Human Emotions to Suggest Suitable Music as per Individual's Mood

There is music recommendation software and music providers that are well explored and commonly used, but those are generally based on simple similarity calculations and manually tagged parameters. This project proposes a music recommendation system based on emotion detection of users, automatic computing, and classification. Music is recommended based on the emotion expressed and temper of the user. Like artists and genre, emotion of the user can also be a crucial recommendation point for music listeners. The different mооds in whiсh the system will сlаssify the imаges аre hаррy, neutrаl, аnd sаd. The system will рre-sоrt the songs according to their genre in the above-mentioned categories. This research project gives us advancement in the music industry with the help of machine learning and artificial intelligence and will reduce the hassle of selecting songs in our leisure time and will automatically play songs by detecting the emotion of the user. This data can be used to play the songs that match the current mood detected from the provided input by the user.