研究目的
To propose an efficient way to detect sarcastic tweets on Twitter and study how to use this information to enhance the accuracy of sentiment analysis.
研究成果
The proposed pattern-based approach for detecting sarcasm on Twitter shows good results, with an accuracy of 83.1% and precision of 91.1%. The study highlights the importance of pattern-based features in detecting sarcastic statements and suggests that using a bigger training set could improve results.
研究不足
The approach may not cover all possible sarcastic patterns due to the relatively small size of the training set. The informal language and noise in Twitter data can affect the performance of part-of-speech tagging.
1：Experimental Design and Method Selection:
The study proposes a pattern-based approach to detect sarcasm on Twitter, using four sets of features for classification.
2：Sample Selection and Data Sources:
Tweets were collected using Twitter's streaming API, focusing on those containing the hashtag "#sarcasm".
3：List of Experimental Equipment and Materials:
Tools used include Apache OpenNLP for NLP tasks and Gate Twitter part-of-speech tagger for tagging tweets.
4：Experimental Procedures and Operational Workflow:
Features were extracted from tweets, including sentiment-related, punctuation-related, syntactic and semantic features, and pattern-related features.
5：Data Analysis Methods:
Classification was performed using the toolkit weka and libsvm for SVM classification.
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