TOM: Twitter opinion mining framework using hybrid classification scheme
TOM: Twitter opinion mining framework using hybrid classification scheme
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DOI：
10.1016/j.dss.2013.09.004
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发表时间：
2014-01-01
影响因子：
7.5
通讯作者：
Qamar, Usman
中科院分区：
文献类型：
--
作者：
Khan, Farhan Hassan;Bashir, Saba;Qamar, Usman
Twitter已经成为最近最流行的微博平台之一。数以百万计的用户可以在微博平台上分享他们对不同方面和事件的想法和意见。因此，Twitter被认为是决策和情感分析的丰富信息来源。情感分析是指一种分类问题，其主要重点是预测词语的极性，然后将其分类为积极和消极的情感，目的是识别以任何形式或语言表达的态度和观点。Twitter上的情感分析为组织提供了一种快速有效的方法来监测公众对他们的品牌，业务，董事等的感受。近年来，人们研究了各种用于训练Twitter数据集情感分类器的功能和方法，并取得了不同的结果。以前的技术中的主要问题是分类准确性，数据稀疏性和讽刺，因为它们错误地分类了大多数推文，其中很高比例的推文被错误地分类为中性。本文针对这些问题，提出了一种基于混合方法的twitter源分类算法。所提出的方法包括在将文本馈送到分类器之前的各种预处理步骤。实验结果表明，该技术克服了以往的局限性，并取得了更高的准确性相比，类似的技术。(C)2013爱思唯尔有限公司版权所有。
Twitter has become one of the most popular micro-blogging platform recently. Millions of users can share their thoughts and opinions about different aspects and events on the micro-blogging platform. Therefore, Twitter is considered as a rich source of information for decision making and sentiment analysis. Sentiment analysis refers to a classification problem where the main focus is to predict the polarity of words and then classify them into positive and negative feelings with the aim of identifying attitude and opinions that are expressed in any form or language. Sentiment analysis over Twitter offers organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. The primary issues in previous techniques are classification accuracy, data sparsity and sarcasm, as they incorrectly classify most of the tweets with a very high percentage of tweets incorrectly classified as neutral. This research paper focuses on these problems and presents an algorithm for twitter feeds classification based on a hybrid approach. The proposed method includes various pre-processing steps before feeding the text to the classifier. Experimental results show that the proposed technique overcomes the previous limitations and achieves higher accuracy when compared to similar techniques. (C) 2013 Elsevier B.V. All rights reserved.