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Music recommendation system based on user's sentiments extracted from social networks
In recent years, the sentiment analysis has been explored by several Internet services to recommend contents in accordance with human emotions, which are expressed through informal texts posted on social networks. However, the metrics used in the sentiment analysis only classify a sentence with posi...
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Published in: | IEEE transactions on consumer electronics 2015-08, Vol.61 (3), p.359-367 |
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creator | Rosa, Renata L. Rodriguez, Demostenes Z. Bressan, Graca |
description | In recent years, the sentiment analysis has been explored by several Internet services to recommend contents in accordance with human emotions, which are expressed through informal texts posted on social networks. However, the metrics used in the sentiment analysis only classify a sentence with positive, neutral or negative intensity, and do not detect sentiment variations in accordance with the user's profile. In this arena, this paper presents a music recommendation system based on a sentiment intensity metric, named enhanced Sentiment Metric (eSM) that is the association of a lexicon-based sentiment metric with a correction factor based on the user's profile. This correction factor is discovered by means of subjective tests, conducted in a laboratory environment. Based on the experimental results, the correction factor is formulated and used to adjust the final sentiment intensity. The users' sentiments are extracted from sentences posted on social networks and the music recommendation system is performed through a framework of low complexity for mobile devices, which suggests songs based on the current user's sentiment intensity. Also, the framework was built considering ergonomic criteria of usability. The performance of the proposed framework is evaluated with remote users using the crowdsourcing method, reaching a rating of 91% of user satisfaction, outperforming a randomly assigned song suggestion that reached 65% of user satisfaction. Furthermore, the paper presents low perceived impacts on the analysis of energy consumption, network and latency in accordance with the processing and memory perception of the recommendation system, showing advantages for the consumer electronic world. |
doi_str_mv | 10.1109/TCE.2015.7298296 |
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However, the metrics used in the sentiment analysis only classify a sentence with positive, neutral or negative intensity, and do not detect sentiment variations in accordance with the user's profile. In this arena, this paper presents a music recommendation system based on a sentiment intensity metric, named enhanced Sentiment Metric (eSM) that is the association of a lexicon-based sentiment metric with a correction factor based on the user's profile. This correction factor is discovered by means of subjective tests, conducted in a laboratory environment. Based on the experimental results, the correction factor is formulated and used to adjust the final sentiment intensity. The users' sentiments are extracted from sentences posted on social networks and the music recommendation system is performed through a framework of low complexity for mobile devices, which suggests songs based on the current user's sentiment intensity. Also, the framework was built considering ergonomic criteria of usability. The performance of the proposed framework is evaluated with remote users using the crowdsourcing method, reaching a rating of 91% of user satisfaction, outperforming a randomly assigned song suggestion that reached 65% of user satisfaction. Furthermore, the paper presents low perceived impacts on the analysis of energy consumption, network and latency in accordance with the processing and memory perception of the recommendation system, showing advantages for the consumer electronic world.</description><identifier>ISSN: 0098-3063</identifier><identifier>EISSN: 1558-4127</identifier><identifier>DOI: 10.1109/TCE.2015.7298296</identifier><identifier>CODEN: ITCEDA</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Data mining ; Dictionaries ; Electronics ; Energy consumption ; Measurement ; Mobile Devices ; Mood ; Music ; Recommendation System ; Recommender systems ; Sentences ; Sentiment analysis ; Social Network ; Social network services ; Social networks ; User satisfaction</subject><ispartof>IEEE transactions on consumer electronics, 2015-08, Vol.61 (3), p.359-367</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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However, the metrics used in the sentiment analysis only classify a sentence with positive, neutral or negative intensity, and do not detect sentiment variations in accordance with the user's profile. In this arena, this paper presents a music recommendation system based on a sentiment intensity metric, named enhanced Sentiment Metric (eSM) that is the association of a lexicon-based sentiment metric with a correction factor based on the user's profile. This correction factor is discovered by means of subjective tests, conducted in a laboratory environment. Based on the experimental results, the correction factor is formulated and used to adjust the final sentiment intensity. The users' sentiments are extracted from sentences posted on social networks and the music recommendation system is performed through a framework of low complexity for mobile devices, which suggests songs based on the current user's sentiment intensity. Also, the framework was built considering ergonomic criteria of usability. The performance of the proposed framework is evaluated with remote users using the crowdsourcing method, reaching a rating of 91% of user satisfaction, outperforming a randomly assigned song suggestion that reached 65% of user satisfaction. 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(IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>L7M</scope></search><sort><creationdate>201508</creationdate><title>Music recommendation system based on user's sentiments extracted from social networks</title><author>Rosa, Renata L. ; Rodriguez, Demostenes Z. ; Bressan, Graca</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c324t-793370ef49cc2cb99f7b68ac4b3139bb2a56e7edc1773105d4ceded750ad0b7d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Data mining</topic><topic>Dictionaries</topic><topic>Electronics</topic><topic>Energy consumption</topic><topic>Measurement</topic><topic>Mobile Devices</topic><topic>Mood</topic><topic>Music</topic><topic>Recommendation System</topic><topic>Recommender systems</topic><topic>Sentences</topic><topic>Sentiment analysis</topic><topic>Social Network</topic><topic>Social network services</topic><topic>Social networks</topic><topic>User satisfaction</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rosa, Renata L.</creatorcontrib><creatorcontrib>Rodriguez, Demostenes Z.</creatorcontrib><creatorcontrib>Bressan, Graca</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEL</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on consumer electronics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rosa, Renata L.</au><au>Rodriguez, Demostenes Z.</au><au>Bressan, Graca</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Music recommendation system based on user's sentiments extracted from social networks</atitle><jtitle>IEEE transactions on consumer electronics</jtitle><stitle>T-CE</stitle><date>2015-08</date><risdate>2015</risdate><volume>61</volume><issue>3</issue><spage>359</spage><epage>367</epage><pages>359-367</pages><issn>0098-3063</issn><eissn>1558-4127</eissn><coden>ITCEDA</coden><abstract>In recent years, the sentiment analysis has been explored by several Internet services to recommend contents in accordance with human emotions, which are expressed through informal texts posted on social networks. However, the metrics used in the sentiment analysis only classify a sentence with positive, neutral or negative intensity, and do not detect sentiment variations in accordance with the user's profile. In this arena, this paper presents a music recommendation system based on a sentiment intensity metric, named enhanced Sentiment Metric (eSM) that is the association of a lexicon-based sentiment metric with a correction factor based on the user's profile. This correction factor is discovered by means of subjective tests, conducted in a laboratory environment. Based on the experimental results, the correction factor is formulated and used to adjust the final sentiment intensity. The users' sentiments are extracted from sentences posted on social networks and the music recommendation system is performed through a framework of low complexity for mobile devices, which suggests songs based on the current user's sentiment intensity. Also, the framework was built considering ergonomic criteria of usability. The performance of the proposed framework is evaluated with remote users using the crowdsourcing method, reaching a rating of 91% of user satisfaction, outperforming a randomly assigned song suggestion that reached 65% of user satisfaction. Furthermore, the paper presents low perceived impacts on the analysis of energy consumption, network and latency in accordance with the processing and memory perception of the recommendation system, showing advantages for the consumer electronic world.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TCE.2015.7298296</doi><tpages>9</tpages></addata></record> |
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subjects | Data mining Dictionaries Electronics Energy consumption Measurement Mobile Devices Mood Music Recommendation System Recommender systems Sentences Sentiment analysis Social Network Social network services Social networks User satisfaction |
title | Music recommendation system based on user's sentiments extracted from social networks |
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