Connecting Text Classification with Image Classification: A New Preprocessing Method for Implicit Sentiment Text Classification

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 5; p. 1899
• Main Authors: Chen, Meikang, Ubul, Kurban, Xu, Xuebin, Aysa, Alimjan, Muhammat, Mahpirat
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.02.2022; MDPI
• Subjects: Algorithms; Attitude; Classification; Computational linguistics; Data Collection; data preprocessing; Datasets; Deep learning; Emotions; image classification; implicit sentiment analysis; Language; Language processing; Machine learning; Methods; Natural language interfaces; Natural Language Processing; Sentiment analysis; Text analysis; Text categorization; text classification; China; data preprocessing; text classification; image classification; natural language processing; implicit sentiment analysis
• DOI: 10.3390/s22051899
• ISSN: 1424-8220

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.