Abstract
As a research hotspot in the field of natural language processing (NLP), sentiment analysis can be roughly divided into explicit sentiment analysis and implicit sentiment analysis. However, due to the lack of obvious emotion words in the implicit sentiment analysis task and because the sentiment polarity contained in implicit sentiment words is not easily accurately identified by existing text-processing methods, the implicit sentiment analysis task is one of the most difficult tasks in sentiment analysis. This paper proposes a new preprocessing method for implicit sentiment text classification; this method is named Text To Picture (TTP) in this paper. TTP highlights the sentiment differences between different sentiment polarities in Chinese implicit sentiment text with the help of deep learning by converting original text data into word frequency maps. The differences between sentiment polarities are used as sentiment clues to improve the performance of the Chinese implicit sentiment text classification task. It does this by transforming the original text data into a word frequency map in order to highlight the differences between the sentiment polarities expressed in the implicit sentiment text. We conducted experimental tests on two common datasets (SMP2019, EWECT), and the results show that the accuracy of our method is significantly improved compared with that of the competitor’s. On the SMP2019 dataset, the accuracy-improvement range was 4.55–7.06%. On the EWECT dataset, the accuracy was improved by 1.81–3.95%. In conclusion, the new preprocessing method for implicit sentiment text classification proposed in this paper can achieve better classification results.
Highlights
Sentiment analysis is a basic task in natural language processing
Considering that it is difficult for implicit sentiment analysis to classify implicit sentiment text based on the explicit sentiment dictionary, a new preprocessing method for implicit sentiment text classification is described in this paper
We propose a new preprocessing method for implicit sentiment text classification
Summary
Sentiment analysis is a basic task in natural language processing. Because sentiment analysis can be widely used in specific practical tasks such as comment analysis [1], public opinion analysis [2], mental health analysis [3], recommender systems [4] and spam identification, it has great development prospects. E3 expresses an implicit negative sentiment by describing the fact that the sand in the shoes cannot be cleaned It can be seen from these examples that explicit sentiment sentences contain clear sentiment words. In the face of implicit sentiment text, the existing methods based on sentiment words have difficulty obtaining the implicit sentiment polarity clues contained in the sentence. Considering that it is difficult for implicit sentiment analysis to classify implicit sentiment text based on the explicit sentiment dictionary, a new preprocessing method for implicit sentiment text classification is described in this paper.
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