Ranked Rule Based Approach for Sentiment Analysis

Prajaktee S. Rane, Rubeena A. Khan
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引用次数: 1

Abstract

Today, large population use social networking sites like Facebook, Twitter, LinkedIn etc. Through social media, people share messages, photos. They also impart information about a particular event or specific situation. There is limited research on crowd management to handle a disaster. We should focus on Crowd Management using Sentiment Analysis as a tool for safety in some events or situations. People convey their emotion about crowd using social sites. Crowd-related issues encountered day to day life such as stations, shopping malls, and stadiums or some events like marriage which may cause congestion and due to that some people may be injured or causes death. Peoples post their sentiments through Twitter, LinkedIn etc.In this paper, we consider traffic jam event where traffic will be able to move or will not be able to move. For this purpose, tweets are collected from social networking site Twitter. Human expressions are expressed through Natural Language Processing and then calculate polarity of sentiment using rule-based approach. User’s opinion is classified into positive, negative or neutral Sentiment. Polarity score of sentence is calculated through SND pattern. Users may enter false tweets which will decrease accuracy of system. To increase accuracy of system along with polarity score, we also consider polling based on user ranking in our proposed system.
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基于排序规则的情感分析方法
如今,很多人使用Facebook、Twitter、LinkedIn等社交网站。通过社交媒体,人们分享信息、照片。它们也传递关于特定事件或特定情况的信息。关于灾难处理的人群管理的研究有限。在某些事件或情况下,我们应该把重点放在使用情绪分析作为安全工具的人群管理上。人们通过社交网站来表达他们对人群的情感。与人群相关的问题在日常生活中遇到,如车站,购物中心,体育场馆或一些事件,如婚姻,可能会导致拥堵,因此有些人可能会受伤或死亡。人们通过Twitter, LinkedIn等发布他们的情绪。在本文中,我们考虑交通堵塞事件,交通将能够移动或将无法移动。为此,从社交网站Twitter收集tweets。通过自然语言处理对人类表情进行表达,然后利用基于规则的方法计算情感极性。用户的意见分为正面、负面或中性情绪。通过SND模式计算句子极性得分。用户可能会输入虚假推文,这会降低系统的准确性。为了提高系统的准确性和极性评分,我们还考虑了基于用户排名的轮询。
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