Analysis of Sentiments for Sports data using RapidMiner

被引:0
|
作者
Pawar, Tanuj [1 ]
Kalra, Parul [1 ]
Mehrotra, Deepti [1 ]
机构
[1] Amity Univ Uttar Pradesh, Noida, India
关键词
Sentiment-analysis; RapidMiner; Text-mining; Social-media;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Sentiment analysis basically consist of determining and categorizing opinions expressed on the basis of computational text. It helps us to determine user's attitude towards a specific topic on the basis of positive, neutral and negative. Social media acts as one of the biggest platforms where people express their feelings on variety of issues by writing on websites, blogs, public forums or online groups. Sports nowadays have also become one of the major topics that people discuss on social media. People's opinions are basically positive, neutral or negative, so only looking at each opinion and coming to a result is very difficult and time consuming. In this research paper, an overall process of feature based sentiment analysis is showcased and opinions of people are analyzed regarding cricket matches using RapidMiner.
引用
收藏
页码:625 / 628
页数:4
相关论文
共 50 条
  • [1] Diabetes Data Analysis and Prediction Model Discovery Using RapidMiner
    Han, Jianchao
    Rodriguze, Juan C.
    Beheshti, Mohsen
    [J]. FGCN: PROCEEDINGS OF THE 2008 SECOND INTERNATIONAL CONFERENCE ON FUTURE GENERATION COMMUNICATION AND NETWORKING, VOLS 1 AND 2, 2008, : 1048 - 1051
  • [2] Sentiments Analysis Of Twitter Data Using Data Mining
    Jain, Anurag P.
    Katkar, Vijay D.
    [J]. 2015 IEEE INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING (ICIP), 2015, : 807 - 810
  • [3] Relevance Feedback on Mobile Data Using RapidMiner
    Pawar, Tanuj
    Kalra, Parul
    Mehrotra, Deepti
    [J]. PROCEEDINGS OF THE 2018 4TH INTERNATIONAL CONFERENCE ON APPLIED AND THEORETICAL COMPUTING AND COMMUNICATION TECHNOLOGY (ICATCCT - 2018), 2018, : 166 - 169
  • [4] RapidMiner Framework for Manufacturing Data Analysis on the Cloud
    Kitcharoen, Nopparoot
    Kamolsantisuk, Sasithorn
    Angsomboon, Reenapat
    Achalakul, Tiranee
    [J]. 2013 10TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING (JCSSE), 2013, : 149 - 154
  • [5] Sentiment Analysis of English Tweets Using RapidMiner
    Tripathi, Pragya
    Vishwakarma, Santosh Kr
    Lala, Ajay
    [J]. 2015 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMMUNICATION NETWORKS (CICN), 2015, : 668 - 672
  • [6] Movie Review Summarization and Sentiment Analysis using RapidMiner
    Alsaqer, Alaa F.
    Sasi, Sreela
    [J]. 2017 INTERNATIONAL CONFERENCE ON NETWORKS & ADVANCES IN COMPUTATIONAL TECHNOLOGIES (NETACT), 2017, : 329 - 335
  • [7] Mining the Web of Linked Data with RapidMiner
    Ristoski, Petar
    Bizer, Christian
    Paulheim, Heiko
    [J]. JOURNAL OF WEB SEMANTICS, 2015, 35 : 142 - 151
  • [8] World Cup 2014 in the Twitter World: A big data analysis of sentiments in US sports fans' tweets
    Yu, Yang
    Wang, Xiao
    [J]. COMPUTERS IN HUMAN BEHAVIOR, 2015, 48 : 392 - 400
  • [9] TED-S: Twitter Event Data in Sports and Politics with Aggregated Sentiments
    Hettiarachchi, Hansi
    Al-Turkey, Doaa
    Adedoyin-Olowe, Mariam
    Bhogal, Jagdev
    Gaber, Mohamed Medhat
    [J]. DATA, 2022, 7 (07)
  • [10] Analysis of Sentiments of Twitter Data on Remote Working
    Goyal, Komal
    Nigam, Ashutosh
    Goyal, Neha
    [J]. Smart Innovation, Systems and Technologies, 2022, 283 : 449 - 455