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Ranked Rule Based Approach for Sentiment Analysis

机译:基于排序规则的情感分析方法

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摘要

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.
机译:如今,大量人口使用诸如Facebook,Twitter,LinkedIn等社交网站。人们通过社交媒体共享消息,照片。他们还传递有关特定事件或特定情况的信息。关于应对灾难的人群管理的研究很少。在某些事件或情况下,我们应该专注于使用情绪分析作为安全工具的人群管理。人们使用社交网站传达对人群的情感。与人群相关的问题在日常生活中遇到,例如车站,购物中心和体育场,或者诸如结婚之类的事件,可能会导致交通拥挤,并导致某些人受伤或死亡。人们通过Twitter,LinkedIn等发布自己的观点。在本文中,我们考虑了交通拥堵事件,其中交通将能够移动或将无法移动。为此,从社交网站Twitter收集了推文。通过自然语言处理来表达人的表情,然后使用基于规则的方法来计算情感的极性。用户的意见分为正面,负面或中性情绪。通过SND模式计算句子的极性得分。用户可能会输入错误的推文,这会降低系统的准确性。为了提高系统的准确性以及极性评分,我们还考虑在建议的系统中基于用户排名进行轮询。

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