Identification of Item Features in Microblogging Data

Abstract

In recent years microblogging services have become very popular and almost everyone have smartphones or tablets Accordingly it attracts more and more users to share their daily life on the platforms The larger volume of real-time information generated by millions of users more important to extract useful information from the microblogging services will be Many existing works could analysis the users’ opinions in microblogging and try to realize how popular the products or items are They usually directly assign the opinion polarity to the items; however we could not know what features make the items become very popular in public In this work we want to use opinion mining to find the relevant and significant features of items from the microblogging services like Twitter We construct a sentiment-based framework to identify the relevant features in microblogging Our method consists of two stages First the data process stage processes the raw data from microblogging services Then in second stage we extract the relevant features by the sentiment characteristics from these messages and utilize these extracted features to construct the relevant feature network by Pointwise Mutual Information and group them according their concepts relations Therefore our system could be applied for knowing the characteristics of a product quickly and explicitly In our experiments our system can identify the popular item features in different domains effectively and the same concept features can cluster together in small groups We invited five users to estimate the results and it shows that our final method is generally greater than baselines even using different threshold In addition another three users estimated the performance of feature grouping and the results in different domains show our method can do well averagely

Date of Award	2015 Apr 22
Original language	English
Supervisor	Hung-Yu Kao (Supervisor)