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Parsing Based Sarcasm Detection from Literal Language in Tweets

机译：在推文中的文字语言中解析基于的讽刺检测

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Objective: To investigate the impact of sarcasm in analyzing the sentiments from tweets.Design: 1. The Tweets related to five different domains are collected from the Twitter by creatingTwitter developer account.2. The Tweets are preprocessed in order to extract the features (Term Frequency, Entropy, Gain Ratio)from the Tweets.3. Proposed an Iterative algorithm in updating the dictionary with Negative Phrases and sentimentwords.4. Assigned a polarity to each tweet using the Dictionary based approach.5. Tweets with Zero scores are detected as Sarcasm tweets.6. Analysis on Variance (AOV) Test is performed on the scores obtained.7. Perform prediction on the scores using Machine Learning Algorithms.8. Estimated the Mean Square Prediction Error (MSPE) using Cross Validation.Outcome: The impact of sarcasm on sentiment analysis is measured in terms of Precision, Recalland F-score.Results: 1. Scores of Tweets based on sentiment words and Negative Phrases.2. Summary of Analysis on variance on Scores obtained.3. Performance of Machine Learning Algorithms in Detecting the sarcasm from tweets.Conclusion: This paper has been presented to put forth the hypothesis that changes sentimentpolarity (positive to negative) of sentences can be used as a feature for detecting sarcasm withinproduct review length bodies of text. With the level of accuracy achieved, this scoring technique canbe implemented as software for social networks. Also, these efforts may be a useful tool for learningabout patterns in sarcasm or making better dictionaries of offensive words.
机译：目的：探讨讽刺在分析Tweets.Design的情绪中的影响：1。通过创建擦手开发人员账户从Twitter收集与五个不同域相关的推文。推文是预处理的，以便从推文中提取特征（术语频率，熵，增益比率）。提出了一种在更新具有负短语和Sentimentwords的字典中的迭代算法。使用基于字典的方法为每个推文分配了极性.5。零分数的推文被检测为讽刺推文。对所获得的分数进行方差分析（AOV）测试。使用机器学习算法对分数进行预测.8。估计使用交叉验证的均线预测误差（MSPE）。讽刺：在精确，Recalland F分数方面测量了讽刺对情绪分析的影响。结果：1。基于情绪单词和负短语的推文分数。得分差异分析综述。机器学习算法的性能检测Tweets的讽刺。结论：本文已经提出了改变宣传致辞（正为负）句子的假设可以用作检测讽刺审查文本的讽刺综述长度体的特征。随着所取得的准确度，该评分技术可以实现为社交网络的软件。此外，这些努力可能是讽刺中学习模式的有用工具，或者使更好的令人反感词典词典。

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