Data Augmentation for Polarity Sentiment Analysis

Abstract

Twitter produces various colloquial tweets as an open communication platform. Previous research shows the frequency of negative sentences in spoken sentences is twice that of written texts. Negative items in negative sentences can shift the polarity of words with feelings, and leads to wrong classification. Therefore, negation processing is essential for the sentiment classification of tweets generated by Twitter. On the basis of considering the importance of negation which is often ignored in previous work, this paper firstly combines the technique of Conjunction Analysis (CA) with the technique of Punctuation Mark Identification (PMI) to detect the negation clue and its scope more accurately. In addition, We propose the OL-DAWE model. The model uses Data Augmentation (DA) approach to generate the opposed tweet according to the original tweet. The model extends learnable data and learns its polarity from both of positive and negative aspects of a tweet. In predicting the polarity of a tweet, the OL-DAWE model takes the positive (negative) degree of the original tweet into account, and also considers the negative (positive) degree of the opposed tweet. We conduct two experiments on two real-world data sets and analyze the experimental results from the perspectives of accuracy and robustness. We prove the effectiveness of our combined technology in negation processing and show that our OL-DAWE model in the polarity sentiment analysis of tweets is better than the baseline for its simplicity and high efficiency.