短文本情感分类:一种使用mSMTP度量的方法

H. Kumar, B. Harish, S. V. A. Kumar, Manjunath Aradhya
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引用次数: 8

摘要

情感分析或意见挖掘是通过自然语言处理、文本分析和计算语言学来识别个人或社区的意见、情绪、情绪和态度的自动化过程。近年来,许多研究集中在众多的博客、推特、论坛和消费者评论网站上,以确定社区的情绪。由于博客网站或消费者评论网站的字符有限,从社交网站检索到的信息将以简短的非正式文本形式出现。短文本情感分析是一项具有挑战性的任务,由于字符的限制,用户倾向于缩短他/她的对话,这导致了拼写错误,俚语和缩短形式的单词。此外,与常规文本相比,短文本包含更多的存在和不存在的术语/特征。在这项工作中,我们的主要目标是使用新的相似性度量将情感分为积极、消极或中性极性。该方法将改进的文本处理相似度度量(mSMTP)嵌入k -最近邻(KNN)分类器。通过欧几里得距离、余弦相似度、Jaccard系数和相关系数的比较,评价了该方法的有效性。并利用基准数据集与支持向量机、随机森林等分类器进行了比较。分类结果根据准确率、精密度、召回率和F-measure进行评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of sentiments in short-text: an approach using mSMTP measure
Sentiment analysis or opinion mining is an automated process to recognize opinion, moods, emotions, attitude of individuals or communities through natural language processing, text analysis, and computational linguistics. In recent years, many studies concentrated on numerous blogs, tweets, forums and consumer review websites to identify sentiment of the communities. The information retrieved from social networking site will be in short informal text because of limited characters in blogging site or consumer review websites. Sentiment analysis in short-text is a challenging task, due to limitation of characters, user tends to shorten his/her conversation, which leads to misspellings, slang terms and shortened forms of words. Moreover, short-texts consists of more number of presence and absence of term/feature compared to regular text. In this work, our major goal is to classify sentiments into positive, negative or neutral polarity using new similarity measure. The proposed method embeds modified Similarity Measure for Text Processing (mSMTP) with K-Nearest Neighbor (KNN) classifier. The effectiveness of the proposed method is evaluated by comparing with Euclidean Distance, Cosine Similarity, Jaccard Coefficient and Correlation Coefficient. The proposed method is also compared with other classifiers like Support Vector Machine and Random Forest using benchmark dataset. The classification results are evaluated based on Accuracy, Precision, Recall and F-measure.
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