Chinese sentiment classification based on Word2vec and vector arithmetic in human–robot conversation. (October 2021)

• Record Type: Journal Article
• Authors: Jia, Keliang
• Is Part Of: Computers & electrical engineering. Volume 95(2021)
• Publication Date: 2021-10
• Subjects: Sentiment classification -- Human–robot conversation -- Word2vec -- Vector arithmetic -- Similarity calculation -- Dictionary expansion; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107423
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: With the aim of enhancing human–chatbot interactions, this paper presents a novel sentiment classification framework that recognizes both semantics emotion terms and emojis. Underpinned by Word2vec and vector arithmetic, a deep model learns the vectors of emotion words and emojis from a large-scale corpus of conversations. The vectors are then assembled into a dictionary, evaluated with an improved k-means similarity calculation, and assigned to a category of emotions. To accommodate negative grammar constructions, e.g., "not happy" as opposed to "unhappy", a separate module combines the vectors of negatives and emotion words using vector addition. The final sentiment classification results are derived by calculating the similarity between an utterance and the emotion categories. Experiments show this strategy results in multi-emotion and polarity classifications between 3 and 4% more precise than the next best performing baseline classifier. Graphical abstract: Highlights: Use Word2vec to learn the vector of emotion word or emoji from the training corpus. Expand emotion dictionary by similarity calculation. Emotion categories are clustered by improved K-Means. The vector of negative form of emotion words is synthesized by vector addition.