Music Recommendation System based on Emotions in User’s Social Media behaviour

10.36227/techrxiv.14493828.v1 ◽

2021 ◽

Author(s):

Ravidu Perera

Keyword(s):

Social Media ◽

Language Processing ◽

Recommendation System ◽

Media Content ◽

Music Recommendation ◽

Social Media Platforms ◽

Emotional Behaviour ◽

Emotional Conflicts ◽

Processing Techniques ◽

Music Recommendation System

The modern lifestyle makes people more competitive. It can lead to more stressful situations in our lifestyle. With the changes in human emotional behaviour, they tend to share their feelings on social media platforms rather than communicating with relatives. Studies proved that people used to listen to music to avoid emotional situations in their life. But there is no proper way to get the most accurate music to listen to and avoid emotional conflicts. Resolving these conflicts, the music recommendation system based on emotion introduced. It analyses the users' recent social media content and detects the various kind of emotions. To ensure that the suggested music is relevant to users emotions, the lyrics analysing was done using natural language processing techniques to identify the music emotions. Most people pay attention to the meaning of the songs, that was the major reason to consider the emotions of the lyrics.

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Sentiment Analysis of Social Media Content for Music Recommendation

NIGERIAN ANNALS OF PURE AND APPLIED SCIENCES ◽

10.46912/napas.225 ◽

2021 ◽

Vol 4 (1) ◽

pp. 110-120

Author(s):

S Akuma ◽

P Obilikwu ◽

E Ahar

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Recommender System ◽

Emotional State ◽

Media Content ◽

Random Testing ◽

Music Recommendation ◽

Social Media Platforms ◽

High Predictability ◽

Use Of Social Media

There is a growing use of social media for communication and entertainment. The information obtained from these social media platforms like Facebook, Linkedln, Twitter and so on can be used for inferring users’ emotional state. Users express their emotions on social media such as Twitter through text and emojis. Such expression can be harvested for the development of a recommender system. In this work, live tweets of users were harvested for the development of an emotion-based music recommender system. The emotions captured in this work include happy, fear, angry disgusted and sad. Users tweets in the form of emojis or text were matched with predefined variables to predict the emotion of users. Random testing of live tweets using the system was conducted and the result showed high predictability.

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An Exploration of Impact of COVID 19 on mental health -Analysis of tweets using Natural Language Processing techniques

10.1101/2020.07.30.20165571 ◽

2020 ◽

Author(s):

Sohini Sengupta ◽

Sareeta Mugde ◽

Garima Sharma

Keyword(s):

Mental Health ◽

Social Media ◽

Natural Language Processing ◽

Natural Language ◽

Sentiment Analysis ◽

Language Processing ◽

Gold Mine ◽

Social Media Platforms ◽

Processing Techniques

Twitter is one of the world's biggest social media platforms for hosting abundant number of user-generated posts. It is considered as a gold mine of data. Majority of the tweets are public and thereby pullable unlike other social media platforms. In this paper we are analyzing the topics related to mental health that are recently (June, 2020) been discussed on Twitter. Also amidst the on-going pandemic, we are going to find out if covid-19 emerges as one of the factors impacting mental health. Further we are going to do an overall sentiment analysis to better understand the emotions of users.

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Similarity approximation of Twitter Profiles

10.20944/preprints202106.0196.v1 ◽

2021 ◽

Author(s):

Niloufar Shoeibi ◽

Nastaran Shoeibi ◽

Pablo Chamoso ◽

Zakie Alizadehsani ◽

Juan M. Corchado

Keyword(s):

Social Media ◽

Language Processing ◽

User Satisfaction ◽

Intelligent System ◽

Media Analysis ◽

Related Information ◽

Social Media Platforms ◽

Dynamic Time ◽

Content Similarity ◽

Processing Techniques

Social media platforms are entirely an undeniable part of the lifestyle from the past decade. Analyzing the information being shared is a crucial step to understand humans behavior. Social media analysis is aiming to guarantee a better experience for the user and risen user satisfaction. But first, it is necessary to know how and from which aspects to compare users with each other. In this paper, an intelligent system has been proposed to measure the similarity of Twitter profiles. For this, firstly, the timeline of each profile has been extracted using the official Twitter API. Then, all information is given to the proposed system. Next, in parallel, three aspects of a profile are derived. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized for comparison of the behavioral ratios of two profiles. Next, Graph Network Analysis is used for monitoring the interactions of the user and its audience; for estimating the similarity of graphs, Jaccard similarity is used. Finally, for the Content similarity measurement, natural language processing techniques for preprocessing and TF-IDF for feature extraction are employed and then compared using the cosine similarity method. Results have presented the similarity level of different profiles. As the case study, people with the same interest show higher similarity. This way of comparison is helpful in many other areas. Also, it enables to find duplicate profiles; those are profiles with almost the same behavior and content.

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SongRecommend: From summarization to recommendation

Natural Language Engineering ◽

10.1017/s1351324912000253 ◽

2012 ◽

Vol 20 (1) ◽

pp. 29-67

Author(s):

SWATI TATA ◽

BARBARA DI EUGENIO

Keyword(s):

Language Processing ◽

Recommendation System ◽

User Study ◽

Portable Devices ◽

Time On Task ◽

Specific Recommendation ◽

Music Recommendation ◽

On Line ◽

The Impact ◽

Music Recommendation System

In recent years, the availability of too much information has become a fact of life for anybody connected with the Internet. The same is true for music: because of the penetration of portable devices and the availability of millions of tracks on the web, individual music collections have become unwieldy. Users need tools to help search their own song collections, and to recommend songs they may be interested in. Whereas recommendation systems have been developed for a variety of products, a music recommendation system presents special challenges, including the ability to recommend individual songs, as opposed to entire albums, even if only full album reviews are available on-line. SongRecommend, our music recommendation system, combines information extraction and generation techniques to produce summaries of reviews of individual songs from album reviews. We present a number of evaluations for SongRecommend: intrinsic evaluations of the extraction components, and of the informativeness of the summaries; and a user study of the impact of the song review summaries on users’ decision-making processes. When presented with the summary, users were able to make quicker decisions, and their choices were more varied. Whereas the smaller size of the summary has an impact on time-on-task, users do not appear to choose a specific recommendation only based on number of words. Our work demonstrates that state-of-the-art techniques in Natural Language Processing can be integrated into an effective end-to-end system.

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MO’ CHARACTERS MO’ PROBLEMS: ONLINE SOCIAL MEDIA PLATFORM CONSTRAINTS AND MODES OF COMMUNICATION

AoIR Selected Papers of Internet Research ◽

10.5210/spir.v2018i0.10497 ◽

2020 ◽

Author(s):

Lewis Mitchell ◽

Joshua Dent ◽

Joshua Ross

Keyword(s):

Social Media ◽

Language Processing ◽

Large Scale ◽

English Language ◽

Length Change ◽

Reading Level ◽

Online Social Media ◽

Social Media Platforms ◽

Media Platform ◽

Processing Techniques

It is widely accepted that different online social media platforms produce different modes of communication, however the ways in which these modalities are shaped by the constraints of a particular platform remain difficult to quantify. On 7 November 2017 Twitter doubled the character limit for users to 280 characters, presenting a unique opportunity to study the response of this population to an exogenous change to the communication medium. Here we analyse a large dataset comprising 387 million English-language tweets (10% of all public tweets) collected over the September 2017--January 2018 period to quantify and explain large-scale changes in individual behaviour and communication patterns precipitated by the character-length change. Using statistical and natural language processing techniques we find that linguistic complexity increased after the change, with individuals writing at a significantly higher reading level. However, we find that some textual properties such as statistical language distribution remain invariant across the change, and are no different to writings in different online media. By fitting a generative mathematical model to the data we find a surprisingly slow response of the Twitter population to this exogenous change, with a substantial number of users taking a number of weeks to adjust to the new medium. In the talk we describe the model and Bayesian parameter estimation techniques used to make these inferences. Furthermore, we argue for mathematical models as an alternative exploratory methodology for "Big" social media datasets, empowering the researcher to make inferences about the human behavioural processes which underlie large-scale patterns and trends.

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