Detecting trends in product consumer preference using sentiment visualization

As more and more consumers use the web space to voice out and share their opinions, the Internet as a whole continues to grow as a valuable asset especially for companies to support business solutions. This is because consumer opinions are very indicative of consumer preference which aid marketing c...

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Main Author: Chua, Tommy
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Published: Animo Repository 2014
Online Access:https://animorepository.dlsu.edu.ph/etd_masteral/4681
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:etd_masteral-115192021-01-31T01:27:13Z Detecting trends in product consumer preference using sentiment visualization Chua, Tommy As more and more consumers use the web space to voice out and share their opinions, the Internet as a whole continues to grow as a valuable asset especially for companies to support business solutions. This is because consumer opinions are very indicative of consumer preference which aid marketing companies in becoming more consumer-centric. Sentiment Visualization offers its application when dealing with user-generated data to collectively product a general view of consumer preference among the abundant consumer opinions currently available in the web space. Several efforts have delved into graphically representing sentiments across a wide number of texts for users to effectively draw pertinent information on. This research designed a framework that detects trends in product consumer preference through sentiment visualization. To identify consumer preference, consumer reviews were processed to extract product attributes along with their corresponding descriptions through term frequency inverse class frequency and dependency parsing. These information were then aggregated to sentiment visualizations which present and highlight the trend in product consumer preference. The main output of the research was evaluated by comparing it to the actual trends that are seen in the mobile market. Two different evaluations were conducted to better assess the produced framework. The first evaluation method focused on looking at the frameworks capability to detect top attribute descriptions for each year. The research output was able to correlate with the the market movement when only one match was surveyed. This was able to garner an average of 89%. The second evaluation method looked at comparing the performance or progression of each attribute description through peak years. The result for this evaluation was satisfactory as a 70% correlation was attained. Although market research was only assumed, the framework produced by the research was able to present information that mostly correlated with the actual production trends in the market. 2014-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_masteral/4681 Master's Theses Animo Repository
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
description As more and more consumers use the web space to voice out and share their opinions, the Internet as a whole continues to grow as a valuable asset especially for companies to support business solutions. This is because consumer opinions are very indicative of consumer preference which aid marketing companies in becoming more consumer-centric. Sentiment Visualization offers its application when dealing with user-generated data to collectively product a general view of consumer preference among the abundant consumer opinions currently available in the web space. Several efforts have delved into graphically representing sentiments across a wide number of texts for users to effectively draw pertinent information on. This research designed a framework that detects trends in product consumer preference through sentiment visualization. To identify consumer preference, consumer reviews were processed to extract product attributes along with their corresponding descriptions through term frequency inverse class frequency and dependency parsing. These information were then aggregated to sentiment visualizations which present and highlight the trend in product consumer preference. The main output of the research was evaluated by comparing it to the actual trends that are seen in the mobile market. Two different evaluations were conducted to better assess the produced framework. The first evaluation method focused on looking at the frameworks capability to detect top attribute descriptions for each year. The research output was able to correlate with the the market movement when only one match was surveyed. This was able to garner an average of 89%. The second evaluation method looked at comparing the performance or progression of each attribute description through peak years. The result for this evaluation was satisfactory as a 70% correlation was attained. Although market research was only assumed, the framework produced by the research was able to present information that mostly correlated with the actual production trends in the market.
format text
author Chua, Tommy
spellingShingle Chua, Tommy
Detecting trends in product consumer preference using sentiment visualization
author_facet Chua, Tommy
author_sort Chua, Tommy
title Detecting trends in product consumer preference using sentiment visualization
title_short Detecting trends in product consumer preference using sentiment visualization
title_full Detecting trends in product consumer preference using sentiment visualization
title_fullStr Detecting trends in product consumer preference using sentiment visualization
title_full_unstemmed Detecting trends in product consumer preference using sentiment visualization
title_sort detecting trends in product consumer preference using sentiment visualization
publisher Animo Repository
publishDate 2014
url https://animorepository.dlsu.edu.ph/etd_masteral/4681
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