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Extraction of user demands based on similar tweets graph

Published: 15 January 2020 Publication History

Abstract

Twitter is used by many users, and posted tweets include user's straightforward real intention. Therefore, we can obtain various opinions on items and events by collecting tweets. However, since the tweets are posted one after another over time and are represented by characters, it is difficult to grasp the overall picture of opinions on items. Therefore, by visualizing opinions on items, it is easier to grasp the whole picture more clearly. In this study, we collect tweets including item names and construct a graph connecting similar tweets. Then, from the connected component, we attempt to extract expressions related to user demands. Also, when constructing a similar tweet graph, it is necessary to appropriately set the similarity threshold. If the threshold is too low, unrelated tweets will be connected and a connected component will consist of different demand expressions. On the other hand, if the threshold value is too high, the demand expression of the same meaning will be divided as other connected components due to some notation fluctuation. In this paper, by focusing on the occurrence probability of the demand expression appearing in each connected component and defining the purity and the cohesiveness, we propose a method of setting the apropriate similarity threshold. In our experimental evaluations using a lot of tweets for two games "Mario tennis ace" and "Dairanto smash brothers SPECIAL", we confirmed that opinions such as "interesting" or "difficult" can be extracted from similar tweets graph constructed by the appropriate similarity threshold value. We also confirmed that we can overlook the demands related to items.

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  • (2024)Utilizing cognitive signals generated during human reading to enhance keyphrase extraction from microblogsInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10361461:2Online publication date: 1-Mar-2024
  1. Extraction of user demands based on similar tweets graph

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    cover image ACM Conferences
    ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
    August 2019
    1228 pages
    ISBN:9781450368681
    DOI:10.1145/3341161
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 15 January 2020

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    • JSPS Early-Career Scientists
    • JSPS Grant-in-Aid for Scientific Research (B)

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    ASONAM '19 Paper Acceptance Rate 41 of 286 submissions, 14%;
    Overall Acceptance Rate 116 of 549 submissions, 21%

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    • (2024)Utilizing cognitive signals generated during human reading to enhance keyphrase extraction from microblogsInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10361461:2Online publication date: 1-Mar-2024

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