Abstract

Sarcasm detection is considered one of the most challenging tasks in sentiment analysis and opinion mining applications in the social media. Sarcasm identification is therefore essential for a good public opinion decision. There are some studies on sarcasm detection that apply standard word2vec model and have shown great performance with word-level analysis. However, once a sequence of terms is being tackled, the performance drops. This is because averaging the embedding of each term in a sentence to get the general embedding would discard the important embedding of some terms. LSTM showed significant improvement in terms of document embedding. However, within the classification LSTM requires adding additional information in order to precisely classify the document into sarcasm or not. This study aims to propose two technique based on LSTM and Auto-Encoder for improving the sarcasm detection. A benchmark dataset has been used in the experiments along with several pre-processing operations that have been applied. These include stop word removal, tokenization and special character removal with LSTM which can be represented by configuring the document embedding and using Auto-Encoder the classifier that was trained on the proposed LSTM. Results showed that the proposed LSTM with Auto-Encoder outperformed the baseline by achieving 84% of f-measure for the dataset. The main reason behind the superiority is that the proposed auto encoder is processing the document embedding as input and attempt to output the same embedding vector. This will enable the architecture to learn the interesting embedding that have significant impact on sarcasm polarity.

Highlights

  • The rise of social networks such as Facebook, Twitter and YouTube have a significant impact on the emergence of new fields such as sentiment analysis (Korayem et al, 2012)

  • Assuming a content posted by a regular user to express bad experience regarding a particular product, sentiment analysis aims to analyze the words of such a post in order to classify it into negative polarity

  • The results of applying Auto-Encoder for sarcasm detection are depicted

Read more

Summary

Introduction

The rise of social networks such as Facebook, Twitter and YouTube have a significant impact on the emergence of new fields such as sentiment analysis (Korayem et al, 2012). Sentiment analysis is the task of determining a subjective polarity for a particular social network post (Agarwal et al, 2015; Al-Moslmi et al, 2017; Altawaier and Tiun, 2016). Assuming a content posted by a regular user to express bad experience regarding a particular product, sentiment analysis aims to analyze the words of such a post in order to classify it into negative polarity. A new task called Sarcasm Detection has been proposed to detect fake, unbelievable or incorrect information over social networks (Pandey et al, 2019).

Objectives
Methods
Findings
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call